The ChatGPT Tsunami and Its Impact on IT Landscape and New Ecosystem

This is my recent invited talk given to young entrepreneurs on the LLM and ChatGPT ecosystem.  

1. ChatGPT:  “Tower of Babel” for Human Languages

Natural Language Processing (NLP) is the crown jewel of AI. AI is mainly divided into perceptual intelligence and cognitive intelligence, and the leap from perceptual intelligence to cognitive intelligence is mainly reflected in the ability to complete NLP tasks. Human language is the carrier of human knowledge, and mastering language is a gateway to entering human cognitive intelligence. For thousands of years, eliminating language barriers has always been a dream of mankind. Babel in the Bible refers to the tower that mankind wished to build to overcome barriers of human languages, but it was considered to be impossible to build. We NLP practitioners have also been pursuing this dream, hoping to get closer to the final goal of overcoming the language barrier.


However, on November 30, 2022, remember this day, with the official launch of the ChatGPT model by the American artificial intelligence company OpenAI, the Tower of Babel was officially completed! It not only successfully eliminated the language barriers for mankind but also established a bridge between humans and machines. In no time did we all realize that a ChatGPT tsunami had swept across the world.

Why is ChatGPT judged to be the Tower of Babel? Because its language performance is actually more “native” than native speakers: native speakers inevitably have slips of the tongue from time to time, but the large generative language model like ChatGPT is difficult to make such mistakes and seems to be always in line with language habits. From the input side, it can understand any human language. From the output side, it can speak fluently. What is most shocking is that from its language performance, we can observe what is called the “Chain of Thought” (CoT) behind its responses, with certain logical reasoning abilities, giving people the impression of being clear and organized. Behind the input and output is the so-called LLM (large language model, GPT in particular), which is like a bottomless black hole to users. Inside are actually many layers of neural networks, represented internally as multidimensional vectors, which house a ton of knowledge. 

Let’s take a look at how the LLM behind ChatGPT is developed. There are already tons of technical introductions on this topic, and we will briefly describe the underlying principles. Its basis is GPT-3, or more precisely, the latest version called text-davinci-003. This model is first of all extremely large in scale, and its size is believed to have made miracles happen. With billions of tokens as training data, it forms a model with billions of parameters. Research has shown that generic large models will exhibit an “emergence” of certain skills once they reach a certain scale, and these emerging skills can perform well in various multi-task scenarios with minimal prompting. Previously, this phenomenon was generally attributed to the “transformation of quantity into quality”, and it was basically treated as a mystery in philosophical terms. It is like saying that everything is attributed to God’s favor.

In my understanding, it is not that mysterious, but a reasonably natural result as the emergence of multi-task skills has to be based, and can only be observed, on a super-large data model.  This is because otherwise, there is no sufficient space for the model to tune itself based on human preferences. Large language models are learned from text sequences, and their greatest feature is their ability to over-generate, giving many possibilities for subsequent sequences like “chain reactions”, but only a small percentage of these possibilities are desirable and beneficial. Many generations may be shallow, empty, or even toxic. ChatGPT’s breakthrough lies in the meticulous final fine-tuning process, using reinforcement learning as its core, it found an effective method to keep aligned with human preferences. This is like having a huge basin with numerous children bathing inside, and now you want to pour out the bathwater without pouring out the children. It is almost impossible. But if you can afford to lose some, the result is that the water is poured out, with some good children still inside the basin to help the case. The premise of doing this is that the basin must be large. Only super-large data models can achieve this with sufficient abilities left for numerous tasks. For example, what proportion of parallel translated text or of data of question-and-answer pairs is there in a normal language raw corpus? It’s a tiny tiny fraction, and when the data size is small, it is hard to learn the translation or question-answering skills from sequence-based learning. Only with super-large data and model can the small proportion multiplied by a large number of tokens create the necessary conditions and soil for implicit learning of such skills. In a basic model with almost infinite generation possibilities, if enough work is not done in a later stage, the probability of generating useless responses is high. Therefore, “aligning with human preferences” becomes the ultimate goal of fine-tuning. In this process, many children were also poured out, which is called the “alignment tax” in the literature. But it doesn’t really matter, because people can’t see the lost treasures, as long as they see the good results, it’s fine. Large models have enough redundancy and can survive filtering and pruning at all levels. In fact, it is not the large model itself that creates miracles, but the large model prepares a warm bed for miracles to happen.

What makes ChatGPT different from previous large models is that it has carefully planned for reinforcement learning from human feedback. For a generic open system, humans cannot really pinpoint where it is right or wrong, but at least they can say whether the response is good/useful or bad/no-value. Using this type of feedback to reinforce the learning and to fine-tune the large model, ChatGPT suddenly becomes very human-like. Human-machine interaction has changed from humans accommodating machines and having to write code, to machines accommodating humans and understanding human language. This is a huge transformation.

Reinforcement learning is relatively a difficult type of learning algorithm compared with other supervised learning approaches because it involves a long chain and the definition of the ultimate goal is not explicit and direct, but indirect based on the final outcomes. The idea behind training is to suppress the high probability of poor performance in the original model and bring out the low probability gems hidden in the model: the child is the reinforcement target that conforms to human expectations, but not a specific child as the optimization target. In any case, there is no unique answer format in this world, and there is usually no golden standard for a generation. What we have is the fuzzy feedback given by humans based on preferences: this answer is good, that one is nonsense; this one is correct, that one is discrimination. A typical method that can make good use of this terminal feedback is reinforcement learning. Once this feedback loop is established, the model can be continuously strengthened and iterated, and its performance will naturally improve. So, after some meticulous learning from human feedback, on November 30, 2022, the curtain was lifted, and this was the moment when humans witnessed the miracle.

To be honest, I have been engaged in NLP for my whole life, and I never thought I would see such a miracle in my lifetime. It has been three months since ChatGPT was created, and it still feels like a dream. Sometimes I stare at the ChatGPT icon and ask myself, is this the language gateway to the new ecological universe? I have to say that all the signs indicate that ChatGPT has unlimited potential for NLP.

Let’s take a step back and review the contemporary history of the golden decade of artificial intelligence.

Ten years ago, in the ImageNet competition, deep learning overwhelmingly crushed all other machine learning performances in the image field, triggering a landmark neural network revolution. Deep neural networks rely on supervised learning of big data. Since then, we have known that as long as the data is large enough and labeled, deep learning can handle it. After sweeping through image, speech, and machine translation, it encountered the stumbling block of NLP because many NLP tasks do not have large-scale language data with labels.

Five years ago, the NLP field saw the emergence of large language models (LLMs) represented by BERT and GPT. LLM can directly “eat” language without the need for annotations, which is called self-supervised learning in academia. LLM marks the arrival of the second revolution, which pushed NLP to the center of AI and became the core engine of cognitive intelligence. AI finally overcame the dependence on labeled data which had been the knowledge bottleneck for NLP, leaping from perception to cognition.

Three months ago, ChatGPT was born, creating an almost perfect human-machine natural language interface. From then on, machines began to accommodate humans, using natural language to interact, rather than humans accommodating machines, using computer language. This is a groundbreaking change.

From the emergence of LLM to the advent of ChatGPT, it truly externalized both its linguistic talent and its knowledge potential, allowing ordinary people to experience it. Looking back, human-machine interaction and its related applications have been explored for many years, but before ChatGPT came out, it had never really been solved. When the GPT-3 model was launched two years ago, skilled players of us already knew how capable it was. As long as you give it a few examples, it can follow the examples to accomplish various NLP tasks, so-called few-shot learning. It does not require major modifications to the large model or large-scale labeled data. With just a few examples, GPT-3’s potential can be unleashed to accomplish various NLP tasks, which is already amazing as it overcomes the knowledge bottleneck of supervised learning. However, the basic limitations of these amazing performances of LLM are mostly known within a small circle of players, and a language bridge is needed for its true breakthrough. ChatGPT has come forward with its biggest feature, zero-shot learning, which means that not a single labeled sample is needed, and you can directly tell it what to do. After five years of supervised learning and five years of self-supervised learning of the deep neural network revolution, the final result has been delivered, and the ChatGPT Bebel tower has been fully constructed, marking the pinnacle of the golden decade of AI. ChatGPT has since been like a tsunami, stirring up the world and causing a sensation all over. 


Looking at the history of AI from a broader perspective, 30 years ago, the main approach to NLP tasks was through symbolic logic. Symbolic routes and machine learning are the two paths that have alternated in dominance in AI history every 20-30 years, like a pendulum. But in the past 30 years, machine learning has been on the rise as the mainstream, with the deep learning revolution in the last 10 years. The pendulum shows no sign of swinging back. We practitioners have been on a long journey of the symbolic rule system. It is not in the mainstream, rarely even mentioned by anyone, but it has not been lacking in its own innovation with its own differentiated advantages. It is worth noting that the symbolic parser has eventually embraced data-driven empiricism and relies on a pipeline of multiple modules to ultimately deal with the hierarchy of language structures. We call this deep parsing. Similar to LLM, deep parsing consists of many levels (around 50-100 levels) of bottom-up processing. It also first digests the language but parses incoming sentence sequences into internal symbolic graph structures, rather than LLM’s vector representations. Although deep parsing and deep learning take different representation schemes, both empower downstream NLP tasks, one with structures and the latter with vectors, both greatly improving the efficiency of downstream NLP tasks. Of course, LLM is still the stronger player because it not only masters syntax structures but also performs exceptionally well in discourse and computational styles, the former involving long-distance discourse relationships and the latter capturing subtle differences in language expressions.  Discourse and computational style pose a significant challenge to parsers that primarily focus on sentence structures.

There have always been two main lines in AI. In addition to machine learning, there is traditional symbolic logic, which rises to the philosophical height of rationalism versus empiricism. These two paths have waxed and waned over the past 30 years, with machine learning on the rise and symbolic logic disappearing from the mainstream stage, although the industry has never given up on its use. The transparency and interpretability of symbolic logic translate directly into the convenience of engineering fixed-point error correction, which contrasts with LLM’s black-box-like internal vectors. LLM can use retraining to macroscopically improve, or use fine-tuning or few shots to induce. LLM cannot do pinpoint correction or debugging like in surgery. LLM’s lack of interpretability also often causes user concerns and confusion in practical applications. Perhaps one day in the future, the two paths will converge at a point where a new AI revolution will occur.

From the perspective of AGI, we see that almost all models before LLM were specialized, and the narrower the task, the better the performance. One exception is the parser, which is in essence the “symbolic foundation model” in the pre-LLM era, empowering downstream NLP tasks with structures, just like LLM does with vectors. From a more general perspective, the emergence of LLM represents a breakthrough in the development of artificial intelligence towards achieving AGI, or Artificial General Intelligence. AGI has long been a controversial goal, and many scholars, including myself, have doubted or even mocked its feasibility. However, with the advent of LLM five years ago, AGI became more scientifically viable, rather than just a Utopia. OpenAI, which champions AGI, has become the shining star in this field, having delivered a long list of influential LLM general models that include the GPT series for NLP, Codex for code writing and debugging (eventually used for Microsoft’s Co-pilot service), and DALL-E for image generation.

With ChatGPT as the pinnacle, large models have taken over all NLP tasks simply by using natural language as instructions, not only those defined by the NLP community but also many user-defined tasks. Its NLP tasks are completely open. Tasks related to language and knowledge can be attempted in any language, and often the results are immediate and magical at the same time. Someone has listed 49 task scenarios that it can handle, but it can actually do much more than that.  In addition, new scenarios are being discovered all the time. This is an unprecedented phenomenon in the history of AI, which the industry calls “skill emergence”.

We can examine why it is so capable and knowledgeable. Overall, human systematic knowledge is largely expressed in language. Human knowledge is mainly carried in the form of text (written language), and mathematical formulas can be seen as an extension of written language. From a linguistic perspective, human knowledge can be divided into linguistic knowledge and knowledge beyond linguistics. Linguistic knowledge includes lexicon knowledge, syntax, morphology, discourse, style, etc. Knowledge beyond linguistics is a much broader circle with a much wider boundary. Large language models have not yet mastered human knowledge as a whole, and it seems that they have managed to capture some knowledge floating on top of the sea of human knowledge. As for ChatGPT, it can be said that it has mastered almost all of the linguistic knowledge, but only about 20% of human knowledge in general, including common sense, basic logic, and encyclopedic knowledge. It calls for more serious research to quantify it properly, but in the ballpark, it feels like about 20% of the knowledge has been learned, and the remaining 80% is still not within reach. However, the law of large numbers applies here, namely the 80-20 rule, which means that mastering 20% of the knowledge floating on top in effect covers 80% of the scenarios. However, since there is still an 80% knowledge gap, it still pretends to know things it doesn’t from time to time.  Given that, LLM can still reshape the ecosystem and the world if we learn to use its strengths and to handle its weaknesses wisely.

How do we judge whether it has learned and how well it has performed a task? In any NLP task, there is a quality assurance (QA) protocol to follow, which requires at minimum a test set of annotated samples. Currently, ChatGPT uses zero-shot learning (i.e. zero samples), where a random task is assigned to it and once it is done, it moves to a new task, so there is no chance for building a persistent test set.  So its performance on result quality cannot be quantified directly. In such cases when the internal testing protocol is missing or no longer applicable, external methods must be used to evaluate the data quality indirectly, such as customer surveys or using my previous company Netbase’s social listening service to collect customer feedback online. All the external signs indicate that customer satisfaction seems to be over 80%, and in most task attempts, customer needs are met fairly well, at times with nice surprises and miracle-like performance. Another relatively objective external indicator is user stickiness and growth of user accounts.  ChatGPT has set unprecedented records in this regard, with tens of millions of users in just a few weeks. ChatGPT’s customer growth rate exceeds everyone’s imagination.

In conclusion, ChatGPT represents a major breakthrough in the field of natural language processing and artificial intelligence. As a large language model, it has revolutionized the way we approach NLP tasks and has demonstrated remarkable versatility and capability. However, it is important to keep in mind that ChatGPT is not perfect and there is still much work to be done in terms of improving its performance and addressing its limitations.

Despite these challenges, ChatGPT has already had a profound impact on the field of AI and is poised to continue shaping the future of technology in significant ways. As AI continues to evolve and advance, it is likely that we will see more breakthroughs of LLMs that push the boundaries of what is possible and help us achieve even greater levels of understanding and innovation.


Over the last three months, there has been no end of online forums, discussions, and talks about ChatGPT, and there is still no sign of aesthetic fatigue. Recently, the former head of Y Combinator China Dr. Lu Qi came to Silicon Valley to give a passionate speech, which added fuel to the fire. He compared ChatGPT’s revolution to Web-1. As we all know, the iconic brand that represented the first Internet boom was the Netscape browser. Although Netscape did not grow to a large company, it was the internet revolution it started that created giants like Yahoo, Google, and Amazon. A similar revolution occurred in China, giving rise to world-class companies such as Baidu, Tencent, and Alibaba. Lu Qi believes that we are right now in such an era. He said that the roadmap is so clear, and the trend is so obvious that he has absolutely no doubt in his mind. Overall, I largely agree with his view of technological trends and landscape.

ChatGPT marks the emergence of a new era. Some people say that this is the “iPhone moment” or “Android moment” in the history of contemporary information technology and will lead to a brand-new ecosystem. I feel that Lu Qi’s comparison is more comprehensive, as ChatGPT is like the “Netscape browser” that initiated the first Internet revolution. Regardless of the comparison, it is a game-changer.

However, it is essential to note that ChatGPT also has its shortcomings and challenges. One issue that everyone has noticed is the so-called hallucinations, in fabricating details and distorting facts. Although ChatGPT has conquered any form of human language, it has only scraped the tip of the iceberg of cognitive intelligence. Is it possible for LLM to solve this problem completely? In my opinion, the LLM route alone will not solve cognitive intelligence. As mentioned earlier, ChatGPT has only covered about 20% of human knowledge. Even if LLM continues to expand several orders of magnitude in sequence-based learning, in my estimates it can at best reach 40%-50%. The remaining 50% is a deep sea that can hardly be fathomed. The long tail of knowledge is an absolute explosion of combinations, way beyond the reach of sequence-based language learning. The annoying behavior is that for any knowledge beyond its ken, LLM will not hesitate to fabricate it with fake details that appear genuine. This is a severe problem. The accuracy defect of such long-tail knowledge is an inevitable problem for application services based on LLM.

Moreover, there are many other issues that need to be overcome. For example, when a large model empowers downstream scenarios, how can customer privacy and security be protected during the process of calling the large model? This problem has not yet been solved, but it is believed that better solutions will develop in time. The supplier of large models will surely pay special attention to this issue and provide solutions for their ecosystem’s development.

Another issue is the complex reasoning ability. From the conversations of ChatGPT, we observe that it already has basic reasoning ability. The source of this ability is very interesting. It mainly benefits from self-supervised learning of the massive computer code base. The GPT3.5 on which ChatGPT is based has been trained not only on human natural language but also on massive available open source code written in various computer languages on GitHub, and most of the code has corresponding natural language explanations (comments) too. Since computer code is by nature more logical than natural language, this has helped ChatGPT to organize its response and speak more coherently. This was said to be a nice surprise that the developers themselves had not anticipated. However, it currently still has shortcomings in complex reasoning logic. Fortunately, complex reasoning ability is different from the boundless knowledge network. It is a relatively closed logical set, and it is believed that it can be solved in not too far a future (perhaps GPT4 might already be able to handle it?).

Lastly, let’s talk about the progress of multimodal learning. LLM, as the basic model, has been validated in NLP multi-tasking and has performed exceptionally well. After the breakthrough in NLP, the framework for empowering downstream tasks with a basic model began to radiate toward other modalities. This direction of research is very active in the academic field of multimodal learning. Everything is still ongoing. Currently, the level of multimodal learning in practice is still in the stage of prompt engineering. What is lacking is a natural language interface. People who play with prompts in large models for image and music generation already know the huge potential and effectiveness of the basic model. It is very similar to the situation when we played with few-shot prompts in the GPT-3 playground before ChatGPT was born. It can be foreseen that in near future, a smooth natural language interface will emerge, and users will be able to describe the art they desire, whether it is a painting or a song. The work of aligning with human taste is also ongoing. It is predicted that a natural language to image (NL2img) model like “ChatDalle”, similar to ChatGPT, will implement the desired natural language interface. The same trend is bound to happen in natural language to music (NL2music). We are in an exciting new era of AIGC (AI-generated content) for art creation.

Another predictable picture is that based on the trend of multimodal LLM, there will eventually be a unified large model that integrates various modalities and their associated knowledge. The breakthrough of this model barrier will provide critical support for entrepreneurs to utilize LLMs to empower downstream applications in various scenarios. As we all know, whether it is finance, law, or medicine, each major vertical has its accumulated long-standing structured symbolic knowledge base, including the domain ontology and other databases. How to connect to the domain’s symbolic resources involves breaking the domain barrier. It is expected that this barrier will be largely solved in the next two to three years.

2. LLM Ecosystem Facing Reshuffling

The direct impact of the ChatGPT tsunami is that the NLP ecosystem is facing a reshuffle, and every existing information product or service must be re-examined in the context of LLM.

When we first discussed ChatGPT’s impact on IT services, the first thing that came to our mind was how to combine ChatGPT with search technology, and whether it could re-invent search.

Search is traceable, and every returned result is recorded, so it involves no information fusion. ChatGPT is untraceable and excels at information fusion: ChatGPT has no possibility of plagiarism in essence. Every sentence it spits out is novel sequence based on its digested information sources. Apparently, traditional search and ChatGPT have their own respective advantages and disadvantages. Search is the king of information services, ubiquitous, with a very stable business model. Since the rise of search in the Web 1.0 era, the form and mode of search have basically not changed for more than 20 years. In fact, new technologies and entrepreneurs have been trying to challenge search continuously over the years, and the venture capital industry has also been paying attention to potential search subverters that may become the “next Google”, but the status of search has always been unshakable, at least until now. But this time is different. Microsoft has exclusive code authorization for ChatGPT and has boldly launched the so-called “new Bing”. Google, who has dominated the space for so long, has to mobilize urgently and confront it head-on. A drama of search+LLM is unfolding, like a live drama, telling us that although there are still many difficulties to overcome in integrating these two technologies, the trend is unstoppable, and reshaping a new ecology of search is imperative.

In addition to search, those finely polished directional information products and services now face the fate of being re-examined and reformed, including chat, virtual assistants, grammar correction, machine translation, summarization, knowledge Q&A, etc. The representative services in these areas (Siri, Grammarly, etc.) used to have high technological barriers, which have suddenly been lowered.  Although many products are not facing a catastrophic crisis due to years of polishing and user inertia, some may still exist for a long time, after all, they are all on a downhill road. This is a revolutionary victory of general AI over traditional AI. It is something we would not believe feasible before. We used to be so skeptical of the general approach, waiting to see the joke of those who advocated AGI, such as Open AI who managed to launch a series of impressive LLMs (GPT series, Codex, DALL-E) including ChatGPT.

Look at Siri, which was released by Apple 13 years ago. 13 years is longer than the entire golden decade of the deep learning revolution, but Siri has only recently managed to offer 2-round or 3-round conversations. Amazon’s popular product, Alexa, is the same. It has been polished for several years and accumulated so much user data. Now, with the advent of ChatGPT, what will Apple and Amazon do? They must embrace LLMs.

Next is the commonly seen e-commerce customer service. As we all know, Alibaba and’s online after-sales customer service has been polished to perfection. Because after-sales service issues are relatively concentrated, the problem set is not large while the data are large, accumulated over the years. However, customer service is not only limited to post-sales.  In order to handle customer service smoothly, LLM cannot be ignored.

Moving on to education, it’s clear that the ChatGPT model has the potential to revolutionize all education products and services. Anyone developing educational applications will need to reconsider how to embrace LLMs within the framework of the large model. Education itself deals with language, regardless of whether it is related to arts or science. Although the current large model is not particularly strong in science and engineering (yet), this knowledge gap will be filled to varying degrees soon. ChatGPT is sure to disrupt education, while also providing the largest opportunity for modernizing education. Language learning and computer programming education are obvious areas for ChatGPT to shine, as the model itself is a language model. Although its programming abilities are not yet at the level of professional engineers, it is proficient enough in common code formats to assist with programming and with the learning of programming. In fact, Co-pilot, which has been empowered by the GPT codex, has already become an auxiliary tool for more and more programmers.

Stepping back, we are also facing a huge risk, such as fake news. If one wants to promote a company or product, one can now use ChatGPT to generate all kinds of promotional posts that sound convincing. In the future, those online reviews and comments will also be obscured by fake news, as the cost of creating fake news approaches zero. Without proper precautions, all of this could place humanity in a world where truth and falsehood are indistinguishable. All along, we have been talking about the benefits of LLM and how it can empower new ecosystems for productivity explosion. We expect that in the next five to ten years, new international IT giants like a new Google or New Alibaba will emerge under this new ecosystem, leading to a major transformation in the technology ecosystem. But the danger of LLM misuse is equally great. Is mankind ready for it? Clearly not. Of course, this is another topic, and we will leave it there for now.

3. Wave of Mass Entrepreneurship Coming

With LLM (ChatGPT in particular), there are more product forms and services waiting for entrepreneurs to explore.

Regarding this topic, we need to emphasize the unprecedented entrepreneurial conditions brought by ChatGPT. ChatGPT itself has become a testing ground for products. It is a playground with an infinitely low bar that everyone can play in. The low bar is due to the paradigm shift in human-machine interfaces mentioned earlier. For the first time in AI history, machines began to cater to humans, rather than humans catering to machines. Human language, rather than computer code, became the tool for human-machine interaction. The significance of this change for the new ecology of NLP is difficult to overemphasize. In fact, this provides conditions for “mass entrepreneurship”.

Those who have started AI businesses should all have this experience. The most basic condition for a startup team to have a chance of success is that the product manager and the technical leader can work closely together and communicate effectively. The product leader, relying on their market intuition and understanding of customer needs, strives to find the best market entry angle for technology to be transformed into a service and form a product design plan. The feasibility of this design plan needs to be endorsed and then developed by the technical leader. However, often due to different professional backgrounds and knowledge structures, the situation where the product manager and the technical leader talk past each other is not uncommon. Once this situation arises, the startup company is basically doomed to fail.

ChatGPT fundamentally eliminates the problem of talking past each other. Previously, only the technical leader and programmers could verify the feasibility of a plan, but now, the product leader/CXO, engineers, data analysts, and users with different backgrounds and expertise all have a unified platform, ChatGPT, on which they can illustrate product ideas. Everyone can simulate services on it. Not only has the communication barrier between humans and machines been overcome, but also the communication barrier between different teams. The emergence of this thing is a precondition for a product explosion and mass entrepreneurship.

In the United States, hundreds of startups are now exploring ideas of downstream products and services following ChatGPT or the backend LLMs. While the upstream big models are still rapidly progressing, what they are doing downstream is already in active development. There are countless ordinary people sharing their stories online, showing how they can earn 5,000 dollars using ChatGPT in just two or three hours. This kind of sharing means that the entrepreneurial enthusiasm of grassroots people has been mobilized. It seems that everyone can use this opportunity to find an entrepreneurial perspective. Summarizing these grassroots ideas may also lead to new tracks that can be standardized and scaled to meet market demands.

A big model like ChatGPT is ultimately an operating system-level existence. Every AI-related information product and service, especially those related to language and knowledge, cannot do without it. When Intel dominated the market, the famous logo was “Intel Inside”. In the future, it will be “Chat-Inside”, or more accurately, “Chat-In&Out”. Why in and out? When a big model like ChatGPT empowers products, it is both like a waiter and a chef. The waiter can take your order, interact with you, and understand your needs while also doing the cooking and delivering the service. It requires both language talent and knowledge skills. This is what we call the LLM expert workbench, which may be the biggest new ecological form in the next five years and may open countless doors for entrepreneurship. The basic service form is online information services in various industries, whether it is online education, online lawyers, online consultants, online finance, or online tourism. All are aimed at significantly improving service efficiency. With ChatGPT, you only need to hire one expert to replace the 10 experts that were previously needed to handle tasks. The end result is a productivity explosion.

In conclusion, the wave of mass entrepreneurship is coming, and ChatGPT has brought unprecedented entrepreneurial conditions. It has become a testing ground for products with an infinitely low bar that everyone can play in. The emergence of this technology has eliminated communication barriers between humans and machines and between teams, leading to new tracks that can be standardized and scaled to meet market unmet needs. The future of ChatGPT as an operating system-like existence may be the biggest new ecological form in the next five years, called the LLM expert workbench, which open doors for entrepreneurship and will lead to a productivity explosion.

At this point, the application ecosystem seems very clear. The principle is that experts must be the final filter before delivering the results (human judge as final filter). This is the basic setup, but experts may also provide input prompts to inspire LLM to produce better results.

For almost every application scenario, there is a task to create an expert workbench, including supplementing existing products or services, such as every segment of online education, as well as online doctors, lawyers, financial consultants, etc., and exploring previously unthought-of business scenarios. This is a visible transformation or reshuffling of the ecosystem, providing efficient expert advice (expert-in-loop services).

Speaking of workbenches, e-commerce giants have built relatively large customer service workbenches, which were introduced when user needs and satisfaction could not be met with fully automated solutions or with fully manual solutions. Now with LLM, this form can be extended to all online service sectors. The productivity explosion that this can bring about is beyond imagination.

The design concept of “Human as Judge” has been validated for several years in low-code platforms (such as RPA platforms, parser-enabled information extraction platforms, etc.) for its effectiveness and efficiency. Here, we are talking about a completely new form, where humans only need to act as judges to complete the service. It is now entirely possible to create online information service workbenches tailored to various segments or scenarios, with experts sitting in the background. Specifically, the expert’s role is only to make the decision based on their knowledge and experience, especially at the final “go or no-go” moment. Being a judge is much more efficient than being an athlete.


It is worth emphasizing that ChatGPT brings something new as enabling information technology, as it serves both at a backend and a frontend. It can perform well in high-level and low-level tasks, which is why chat is just the surface of ChatGPT, and its essence is a human-machine interface. Its ability to complete various NLP tasks is at its core. With both surface and essence, downstream products or services can be built around it. In the Intel era, computer product brand advertisements were remembered as “Intel inside,” and in the future, the new ecology should be called “chat in&out,” which refers to the new ecology empowered by LLM, not only empowering the human-machine interaction but also empowering the professional services, with only experts providing the final check. In this form, the experts are behind the scenes. To put it another way, LLM is both a waiter and a chef, but an expert needs to review the food and take responsibility before it is served to ensure service quality (such as online doctors, lawyers, consultants, etc.).

In such an ecosystem, the next five years will be a period of explosive growth for online services. Fortunately, the three-year pandemic has greatly promoted the grassroots awareness of online services, helping to cultivate user online habits and develop the market.

While LLM is powerful in terms of breadth of knowledge, it also has its limitations in terms of precision. The key challenge in building an expert-in-loop service is to overcome the precision bottleneck of LLM. The goal is to raise the precision to a level where it does not significantly impact the efficiency of the expert’s work. If at least 1/4 of the results generated by LLM can match the level of a manual expert’s research, then the efficiency of the expert-in-loop service can be ensured. This is a feasible expectation, and the current solutions are not far from meeting this threshold. With this in mind, we conclude that the door to entrepreneurship in the new ecology of LLM has indeed been opened.



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心向辽阔探索不熄 源码资本 2023-02-22 20:09
做个广告,也好刺激一下自己认真做一下 slides,主要给他们的创业者企业家俱乐部的线上“商学院” 讲,帮助他们头脑风暴, 上面提到感兴趣的, 要联系他们 (源码资本/码脑)。

《AI浪潮:chatGPT 搞定了人类语言》

立委:从语言与语言学角度,chatGPT 的的确确证明了自己万能的语言能力。千百年来的人类巴别塔之望终于美梦成真。巴别塔建成了,建成日期2022年11月。这个成就超出了一般意义的里程碑。这是划时代的进步。




霄云:图灵测试 is not for language only, it is end to end “common sense “ test, human intelligence via language.



霄云:单独测 language 是不是 翻译 或者别的 normalization 就可以? @詹卫东



卫东:从出题的角度考虑,是有唯一正确答案的,但语言题还是跟数学题不同,总会有“更多的视角”和“更开放的标准”隐藏着,导致答案很难唯一。 近义词组是考虑了很多因素挑选的,包括句法、搭配、语义协同、常识等。

立委:语言理解能力可以看 同样的意思 你变着花样不同问法,然后看他的回应。体验下来 结论是 它是真理解了 不比人差。


Li Chen:整体性其实是神经网络的强项,毕竟最后都变成向量了。难的反倒是细节。


Li Chen:我觉得这个现象可以理解。因为像24点这种东西,某种意义上讲就是一个特殊的游戏,需要说明规则,理解规则的基础上来玩。chatgpt真的理解这个规则了么?这个感觉也就是toB难的地方,不同行业的规则不一样,通用模型没见过这么多具体的规则。即便是人,有很强的学习能力,换个行业也得学习工作一段时间才能玩得转。




立委:这个实验好。语言理解从效果上看就是要鲁棒有包容,同一个语义可以有多种不同的表达形式,表达形式不规范也没关系,只要上下文的关键词及其相谐性可以让语句的意义有区别性就好。chatGPT 这方面游刃有余,总是可以把同义的不同说法映射到语义空间的同一个区域。










Li Chen:是的,也许真有个国家地区或者可以当主语,修饰语的确实叫乌兰克。


立委:关于文法书上强调的带有星号 * 的反例,那不是为了语言理解,主要是从语言生成的角度,实践中追求的是合法和地道(nativeness),理论上追求的是 internal grammar/language,需要防止反例出现。

从语言生成角度,LLM 的大数据回归的属性天然实现了 nativeness,反例不仅少见,即便出现,统计上也沉底了。语言生成能力的效果观察,可以让它生成几次,看回应是不是还在同类水平上,是不是走题或掉链子。这一关表现不错。除了特别的风格输出(例如洋泾浜:这种“风格”可以看成 sub-language,里面的正例恰好是规范英语的反例)外,它是不会出现低级文法错误和违背习惯用法的笑话的。所以 native speakers 听着也觉得舒服。



当然,在 input 较短 context 不足以确定内容完整性的的时候,有些反例会呈现歧义或甚至与原意相左的语义,这时候形式的违规的确与内容的混乱或不确定发生关联了。这时候,句法手段的修正(例如次序的调整、功能词的使用以及西方语言中的形态的正确应用等)才会有实质性意义,而不仅仅就是为了 native speaker 听上去顺耳而已。

解析和理解的能力,LLM 特别宽容鲁棒,主要是它的 embedding(编码嵌入,成为其内部的向量表示)可以容纳很长的 input,在 context 相互邻近的关键词之间相互制约下(我们叫篇章中的 semantic coherence,包括词义之间的搭配关系),形式上的偏离规范已经不影响它在语义空间的意义定位,从而“它”可以轻易与“非它”区分开来。

一个符号串 吃进去就是向量空间的某个或某组位置 其意义表现在与其他位置的距离和区别。因此 位置偏差一点 不影响意义 只要它与其他的不同意义的符号串映射可以区别开来。鲁棒性根植于此。换个角度 意义不是要问是什么,更要紧的是 不是其他(什么),只要能维持这种意义空间的区别性,规范不规范就都可以包容。区别之间有足够的空间/距离,即可容忍局部的种种口误 错误。

霄云:Llm 的 position encoding is linearly attached not cross product,so it is a weak form 

立委:词序影响意义的机会不大。当年 一包词模型用了很久 也是因为 词序是较弱的约束,构成区别要素的场景并不频繁。

我把一句话,完全反过来,从:explain quantum computing in simple terms 映射成类似回文:terms simple in computing quantum explain,它毫不迟疑。

人家训练的是next token,现在是处处反着来,本想让它找不着北,但实际上一点也不影响它的“理解”。就是说,当一个模型可以对较长的 input string 做编码嵌入的时候,次序的约束已经很弱了。因为那一小袋词之间的物理距离(proximity constraints)加上它们语义的相谐性(semantic cosntraints)已经足够让这个整体的语义表示与其他对象区分开来,这时候纯粹语言学意义的句法约束(syntactic constraints,包括严格的词序)就可以松绑。

我怀疑 position encoding 即便不做,LLM 也不见得性能会下降很多。

霄云:Could be, popular code base all use it still

立委:换句话说,在 bigram / trigram 建模的年代,词序是重要的 (“我爱她”与“她爱我”,“打死”与“死打”,可不是一回事)。到了ngram 中 n 可以很长的时候,ngram list 与 ngram set 已经语义相等了。



Li Chen:想想确实是这个道理,在有很多词的情况下,还要能组成符合语法的句子的可能性是有限的,也就意味着语义差异不大了。所以这个时候顺序确实已经不重要了,估计这个也是为什么即便是最简单的bag of words也能用来做相似度计算,一用就是几十年的道理。



川:LLM 没问题,ChatGPT is evil

Who is the master, machine or man?

立委:那是因为 chatGPT 太 human like,搞定了自然语言形式。


A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

立委:机器都是假象,AI 本性。Artifical 与假象可以看成是同义词。就本质而言,人工智能就是智能假象,这个论断没有问题,但这应该并不妨碍人类深度使用AI。


三个月玩 chat 下来,我在它生成的英语中,没有发现过语言的问题(内容的毛病不算),一例也没有。但在其中文的生成中,偶然还是会发现它有语言的瑕疵(不符合规范或习惯的用法),虽然它的中文生成能力已经超过多数同胞。这说明,目前 chat 语言训练的中文语料还可以进一步扩大,从爱挑剔、追求完美的语言学家视角,它还有一点点剩余的进步空间。

结论还是: chat 搞定了人类语言,无论听还是说,妥妥的。万能的语言巴别塔是真滴建成了。




A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity


李维 郭进《自然语言处理答问》(商务印书馆 2020)



chatGPT 网址:需要注册)

《AI浪潮:chatGPT 能写出段子吗》





By the way 国内脱口秀这几年蓬勃向上,有超越传统相声的态势,尤其是在年轻人中开始流行。这是以前没想到的,有传统相声的国度,居然让外来艺种抢了风头。制度接轨那么难,艺术接轨如此自然,水到渠成?


gou (go) 我不会飞,可我很快。
niu 所以我那么大(大妞儿?)

猫猫 miao 或 mao, 耗子 mou,也蛮形象,有声有色的样子。

哈,看来只学会了一个套路:羊/yang (young),所以我害羞。






还有一个类似的感受,国内流行乐坛中的 rap 在大唐比想象的流行要广。在一个有数来宝的国度,rap 一样长驱直入。




立委:觉得文化的融合与流行 不是想象的那么难。

国内那些 rap,牵强的说辞泛滥,听着好别扭,觉得比虽然低俗但顺溜地道的数来宝或山东快书,是一种严重退步。但是我们的“成见”挡不住新一代的热情和迷恋,这里面可能有什么文化密码和奥秘。


陪女儿小时候看迪斯尼长大,没想到后来迪斯尼就被 anime 碾压了。anime,我不入,搞不清里面的奥秘。是为沟。



李维 郭进《自然语言处理答问》(商务印书馆 2020)



chatGPT 网址:需要注册)

《AI浪潮:chatGPT 的里程碑意义》

说到chat里程碑的意义,盖茨比作电脑、互联网后的第三大里程碑,显然有点夸张了。可是我们进入计算机博物馆看里程碑展馆,有 1. 第一次下国际象棋打败人类 2. IBM 沃森问答打败人类,后面的还该有一系列打败人类的里程碑吧,例如围棋。

不得不佩服它条理化的能力,只有一个不妥:医学并入了教育。其余的综合 总结能力强过一干人,自然包括在下。在这一长串中,AI明星 chat 可以成为 top 几?

top 10 有点高抬了,top 20 似乎有余:就凭他建成了巴别塔,搞定了人类语言。

文字 应该是 语言/文字。宗教不该漏。





李维 郭进《自然语言处理答问》(商务印书馆 2020)



chatGPT 网址:需要注册)

《AI浪潮:chatGPT 写的情书能有真情吗》



ChatGPT 写情书也不重样。

立委:这是陷入爱河但苦于笨嘴拙舌的人的福音了。人的爱意 哪怕是套话 也是要表达才行,藏在心里不行。

“i love you” 是鹦鹉学舌完全可以做到的,但并不因为是套话而失去其神奇效应。无数的情感矛盾和关系恶化 其实就是缺少了这三个字的表达频度。

但热恋要求更高一点,需要营造浪漫。营造需要形式,而有的人就是不懂形式,不善言辞,不会表达(俗话说,不会来事儿 lol)。你便是真情如海,但羞于表达或表达不出来也没戏。谁愿意与木头谈一场恋爱?


chatGPT 不过就是个工具,就跟你用毛笔还是钢笔一样。这个工具见识过无数的情书。工具帮助你产生形式,至于真情表白还是虚情假意,那要看使用工具的人了。

劝热恋中的人都去订阅 chatGPT pro,现在出来了,每个月20美元,太平价了,可以帮你制造浪漫的条件,无论是诗歌、两地书还是策划。

— *声明:以上是脑残广告,不当真的 =)

顺着这个话题延伸一下,说说 chatGPT 作为文字助手的作用,尤其是对于不善言辞的人。

出口成章的人是少数。见过很多人在一些场合 需要应景 却憋不出话来 十分窘迫。现在好了。不知道有没有办法把 chat 制成一个可以植入的东西,就不说老马说的脑机接口了,只要能让它成为一个隐藏的招之即来 但无人察觉的暗器,也许作为穿戴设备,例如传说中的苹果眼镜,让它编制的应景台词,跟提词器似的,崩到眼镜上,我见人不见。那会是社恐人士多大的福音。

不同程度的社恐据报道是一个非常常见的问题,我自己也深受其害,人稍多就哑巴了,插不上话,却要硬着头皮应付。看社交场合如鱼得水的人 知道他们是胡喷 但人家给气氛啊 自己啥贡献也没有。成为社交累赘,有情商的的人,还要照顾你情绪,不时还要引一两句给你,带你玩的意思。chat 可以改变这一切 让笨嘴拙舌秒变伶牙俐齿,让只懂本行的老专家也能成为百科地保。 

为民:一位圈外朋友的朋友圈信息: “ChatGPT是中庸主义者的福音,完美地让你泯然众人、符合社会的基本期待。


如果用ChatGPT写情书,说明你根本不爱收到情书的对象。但是也许你并不需要soul mate(不是每个人都需要),你只想要应付相亲对象。



情商这东西 为什么人学起来那么笨 机器却行:估计主要是人自我中心 换位思考就难。机器根本没有自我 调教对齐一下就乖巧了。


立委:情商优者治人 智商优者治于人。外行领导内行 由来已久 天经地义。

数量上也不成比例 情商强的人 远远少于智商好的,最后大多做了各级领导或企业老板。




李维 郭进《自然语言处理答问》(商务印书馆 2020)



chatGPT 网址:需要注册)

《AI浪潮:LLM 凭什么能“涌现”超级能力?》



立委:先提一句,zero-shot 等,翻译成“零下、一下、几下”不大好理解,主要是 “下” 是个太常用的汉字,感觉不如 “零样例、单样例、多样例”,或“零剂量、单剂量、多剂量”,甚至“零射击、单射击、多射击” 来得贴切。

为民:这个主要觉得与”shot” 同音,将错就错[Grin]


对于貌似无止境的 S阶梯形跃升(颠覆,涌现),现在大多是观察的归纳总结。为什么会这样,为什么发生超出想象、不可思议的现象和超能力, 很多人觉得是个谜。

以前很多年的AI统计模型(以及符号模型)的归纳总结都是,随着数据的增长,模型就会遭遇天花板,趋向于 diminishing returns,也就是说只有一个 S,不存在上图所示的阶梯形多个S状。


可这一常规却在可以深度学习不同层次注意力patterns的巨量参数模型中突然被打破了。于是 奇迹“涌现”了。


1. 学习对象必需有足够的可学的内容:自然语言正好满足这个条件。

以前我们做NLP的学习任务,一律是单一的,学习 parsing 也好,抽取信息也好。单一的任务角度,可学的目标是相对有限的,数据量的无限增长不可能带来无限可学的标的,因此学习过程遵循单S趋势,跟爬山似的,快到山顶的时候,再多的力气也很难带来进步。


LLM 到了 GPT3 的规模,也不过就是划过了知识的冰山一角(前几篇博文提过,毛估估也就 20%左右),这学到的百分之二十知识,从chatGPT的表现看,里面目前涉及几乎全部的语言知识(有词典知识、词法知识、句法知识、篇章知识、修辞知识、风格知识、对话知识、应用文知识、文学知识),外加漂在人类认知上面的基本常识、百科知识、部分逻辑推理知识等。也就是说,从AGI的视角,自然语言本身作为知识/能力的源头和对象,还有很多可以学、但还没学完的内容。仰望星空,一眼望不到天花板。

2. 学习表示必须有足够的容量:单单对象本身有各种层次可学习的内容还不行,学到了必须有足够的空间放得下才行。这个条件也在不断满足中:在一个billion级参数的多维向量空间中,LLM们的表示空间较之深度学习革命以前的模型是大得太多了。

3. 学习过程必须有足够的深度和层次:这个条件也具备了,拜深度学习革命带来的多层网络所赐。尤其是 transformer 框架下的LLM内的注意力机制所赋能的学习和抽象能力,非以前模型可比。


这一切要落实到实处,implementation level 自然要靠海量的计算条件和工程能力。大厂,或由大厂做后盾的团队(例如 Open AI),具备了这样的软硬件能力。

于是,chatGPT 诞生了。

为民:我这个东西只是点到为止。还有很多东西值得进一步考虑,比如 Transformer 非常神奇。Anthropic 通过分析和实验发现,Transfornmer 的Attention Layer 可以激发 In-Context Learning 能力。而后者是 Prompt-based learning 的关键。




为民:In-Context learning 需要了解清楚。这个被认为是大模型的 emergence 能力。 这个解释也有很多。除了Anthropic 的解释外,还有Stanford 的基于 Bayesian 推理的解释也说得通。

这个in-context learning 也只(碰巧)对人类够用了,它还只是 interpolation, 或者刚好在 extrapolation 的边缘。我感觉顾老师的几何理论接下去可以去解释清楚了[Grin]

立委:是啊,这是 few shots 的奥秘。

few shots 既然没有线下的微调训练,怎么就凭着几个例子,跟人类一样能举一反三,现场就学到了 open ended 的任务呢?只能说这些能力LLM都已经蕴含其中,few shots 就是把蕴含在内的能力激发出来,并现场调适对齐。这已经足够的神奇和不可思议。可是到了 instructGPT 和 chatGPT,few shots 的模式和能力却放到一边了,进阶到了 zero shot,完全的概念化。这已经是 “beyond 神奇”了!

当然,这个 zero shot 的奥秘宏观上讲就是所谓人类对齐(RFHF)的功劳。可到底是怎么奏效的,还是雾里看花。读了 instruct GPT 的论文n遍,所说的与人类偏好对齐的各种操作虽然设计精巧细致,但毕竟对齐工作的数据只是原大数据的一滴水而已,居然有点石成金之效,让人惊掉下巴。

为民:这个我还是欣赏John Shulman,他真将离线 RL 用活了。

立委:本来以为他们会沿着 few shots 的路线,把革命进行到底呢。毕竟 few shots 已经把需要大数据标注的知识瓶颈给“解围”了,prompt engineering 也符合低代码的大趋势,前景足够诱人。比起传统的有监督学习(包括深度有监督学习)不知道要高明多少。谁料想他们一转弯即刻就瞄准了 zero shot 去吊打自然语言以及NLP,爽快利落搞定了人机接口,这个弯转的,简直是神来之笔。

如果坚持 few shots 虽然也还算很大的创新,但绝不会引起chatGPT这样的核弹效应。也不会让无数人浮想联翩,让大佬如比尔盖茨对其几乎无限拔高:堪比电脑发明和互联网问世。

为民:这个是不是 OpenAI 首先(在GPT-3 paper)明确提出这个?这个提法应该不trivial

立委:不知道谁发明的,但肯定是 GPT3 (playground)与 DALL-E 2 以后才广为人知的。prompt engineering 成为热词,形成小圈子的热潮也主要是 open AI 的功劳。

给我们科普一下 学习中的 interpolation VS extrapolation 机制吧。举例说明 

为民:简单说,interpolation (插值) 是预测的点在样本空间里。extrapolation 则在外。足以让人沮丧的是: LeCun 和他的博士后证明,对于高维空间预测问题(大模型属于这个),几乎都是extrapolation 问题。高维问题很难直观解释。


@Wei-Min Lu/MoPaaS 希望这是靠谱的,没有参杂胡说。




我觉得interpolation 和extrapolation 的概念在DL里只(或LeCun这里)是被借用并扩展(



宇宙学里的 “大爆炸”模型,也是外插出来的。所有数据都表明,宇宙婴儿期有一次空间的急剧膨胀。


为民:是的。如果要说真正的 Emergence, 那就得外推(插) 。这个问题不解决,AGI 不可能。所以人类可能无望自己实现。AGI 要靠 ··· AI 自己进化实现[Tongue]。在这之前,人类可能会不断(前仆后继地)宣布实现 AGI 了。


立委:我的体会那是符号泛化操作的前提或公理。分层分级的各种generalizations 都是放宽不同条件 它是有来路 可追踪 可解释和完全可控的。



为民:机器学习的名词千姿百态,很多都是借用其它领域。@白硕 @梁焰

机器学习的外插就是一种 Overfitting, 可能会很离谱,所以外插也不能肆无忌惮啊[Grin]

霄云:有一个细微的区别 符号 in interface or in implementation? 感觉@白硕 老师 说的是 in implementation, 因为界面 输入 输出 依然是符号 只是在计算输出的过程 给 向量化了 。人的处理是不是有时候也这样, deduction and induction r just 符号化 过程 以方便 解释给别人 

有的人是可以知道结果 但是过程解释的不出来。 少 但是见过。chain of thought is related here , 感觉。


霄云:sure. Implementations r in vector space, but is projected back to symbols.

或者 说 我们要逼近的函数 是在符号空间里有定义的 我们的入口在 符号空间里。


霄云:exactly. If it is useful eventually it will be accepted into common

只是它的implementation is done by mapping to vector space and back. And the behavior of that implementation in vector space does suggests some sort of generalization in symbolic space 



霄云:同意 这个view 不是很数学严谨。 我的 function 是 软件开发里的 概念 space 是 loosely used,to make a point about there is a mapping

But for sure the mapping is not one to one , and there are points in vector shape don’t have direct mapping in symbolic space.  So compute is in vector space thus the thing we coined as generalization is implementation in there

立委:如果符号没有足够的空间表示思想 我们如何知道。


霄云:I actually suspect one day that compute can be symbolized , using methods like chain of thought. Language is universal, so it is conceivable that we can ask it to compute following a path that can be symbolically described.

We don’t until we do. Language is not a fixed thing. It is a result of our spending efforts doing something together. It evolves all the time. Just slow enough so feels constant

Brain exist before symbol

立委:那是显然的 低等动物也有brain 但没有(用)符号。但意义不大 。

感知跃升到认知的时侯 符号就与brain纠缠不清了。很难分清先有鸡还是先有蛋。


梁焰:yes, 符号有一个选择,和”去选择(de-select)“的过程,不断反复地这么做。符号思维,大概是人发明的一种高效,省力的思维,但不应该僵化。

霄云:思维 是 什么 ? 计算? 计算in symbolic space? Or compute that can be mapped to some symbolic space ?


霄云:👍,但是我 记得 Hinton说过 neural networks is the compute device 

但是 结果 是 跟 大多数什么意见没有关系 的 ,我们需要这种人。我记得我们都去做 支持向量机的 时候 他 可真的 没有咋追风。

立委:语言符号(除了数学语言和公式)通常漏的跟筛子似的。可是它还是胜任了知识的传承 。靠的就是冗余么?车轱辘话其实每一遍都有一点新意,或不同视角或约束。凑在一起 也一样维持了知识体系的逻辑稳定性 很让人诧异的现象。

道理上,llm 是一种费力而无法完备的路线,看上去就是死路,可是却杀出来迄今最亮眼的认知智能来。这违反我们的直觉,理论上也不好说明。当我们明明积累了浓缩的结构化知识(例如各种知识图谱和数据库),却硬要弃之如履另起炉灶,从粗糙的、重复的、充满了噪音的线性语言大数据去学习认知。正常人应该觉得这是一种疯狂和偏执,妥妥的缘木求鱼、南辕北辙,但现在却似乎是走在正道上,有点侮辱人类智能的感觉。

霄云:对于大多数人来说 哪种计算管用 是 最真实的,然后我们去解释就好了 。反正也没有那么多的 compute [Facepalm] And data。但是 下一个 discovery 又 会又 一个 或者偏执 或者幸运的 人 给找出来,或者 both 。我们 比较幸运的是我们有感知的领域在 发生 paradigm shifting ,so we get to watch at front seat.  Feeling lucky 我们就偷着乐吧[Facepalm]

前几天 看到那个 核聚变的 news ,compare to this one , 想想有些行当可能许久没有什么fireworks ,有感而发。

这个我们可以 go in meaningful discussions or even think how we can make use of it,核聚变 就没有办法了。

立委:当然现在还没有到笑到最好的时刻。也不知道往后的AI认知路上会不会遭遇瓶颈 来阻拦多S形的学习曲线的前行 。毕竟llm只搞定了语言,撬动了认知漂在上面的一个小部分。这样来看AI 的话,乔姆斯基理性主义对于大数据经验主义的经典批判论,似乎仍然站得住。


Why people are fascinated about AI?

General public like it, because they think it’s magic;
Software engineers like it, because they think it’s computer science;
Computer Scientists like it, because they think it’s linguistics or/and mathematics;
Linguists like it, because they think it‘s cognitive science;
Cognitive researchers like it, because they think it’s philosophy;
Philosophers don’t like it, because there is no it.

Meanwhile, Mathematicians like it, because they think it’s mathematics.


And largely true 2

在隔行如隔山的人类认知环境中 每一个专家都有自己的视角,就像我们难免在与机器打交道的时候 常常忍不住高估了机器 读出了AIGC 本身并不具有的意义 。我们在与其他领域专家打交道的时侯 也难免看高或看低了人家。




老顾谈几何 :奇点降临?


李维 郭进《自然语言处理答问》(商务印书馆 2020)



chatGPT 网址:需要注册)






较小的领域先放下,金融是一大块、医疗是一大块、法律也是一大块,教育当然更是一大块。想来最容易被同化的大领域可能就是教育板块了,感觉上,教育这块的领域壁垒没有那么高。而且教育 by definiition 与语言以及语言受众最密切相关,应该/可能是最先被革命的领地。现在高校面对 chat 怪物的惊恐和震撼算是一个本能的反应。

Baojie:DLM 领域语言模型。DLM作为产生领域KB的中间体。



前面提到的这几大领域,其实数据量都很惊人。现在不清楚的是,如果用领域大数据做LLM,是不是就比普适的LLM,如 GPT3,甚至即将到来的 GPT4 就一定更容易落地领域,立竿见影呢?

理论上,与其给超大模型 GPT3/4 做“减法”来做领域模型,不如直接在数据端做工,只跑领域大数据,这样的大模型是不是就好用了呢。不知道,因为这些事还是进行中。


立委:例如 《自然》有一个 article 报道了纯粹利用脱敏的美国电子诊疗记录数据做出来一个 billion 参数的 LLM(A large language model for electronic health records),在8项已有社区标准的医疗NLP任务上都达到或超过了 state of art,大约好了一个百分点左右,不知道落地是不是有感。

另外,前两天注意到微软研究也出了一个医疗 LLM 叫 BioGPT(BioGPT: A Domain-Specific Generative Transformer Language Model Pre-Trained on Large-Scale Biomedical Literature),数据比电子医疗记录要广得多,基本上把医疗卫生的公共数据一网打尽。这些工作刚出来,所用的技术都是LLM积淀下来的框架和路数,对领域落地的影响需要一些时间才能看出来。问题是 这些领域 LLM 本性上还是与领域的图谱和结构化的浓缩资源,井水不犯河水。目前还看不到两个冤家如何融合和协作。


立委:是的。领域数据纯化了,NL 这端,尤其是口语,可能就受影响了。


立委:人家指的是领域这边的能人,还是指的是 AGI 那边的疯子,例如 GPT10?

行业用户再牛,也要面对这个现实:行业里管用的东西基本上处于手工业时代,与 LLM 时代岂止恍如隔世,这种对比和反差 太强烈了,简直让人不忍直视,无法忍受。


立委:嗯 明白了 人家就是隔壁瓦教授。行业用户的牛也明白了,因为它是上帝 有钱 它才不在乎谁对接,谁服务呢。他只要结果 只为结果买单。

广义的对接派包括我们所有不只是玩 LLM,还要用 NLP 来讨好客户的群体,是这个群体最终帮助 figure out 落地形态。从大厂的 LLM 角度看去,所有人都是他家下游无数场景的 practitioners。


立委:这一期与@白硕 上次提到的演讲笔记是同一件事吧?这一期算是铿锵三人行。






很多行业,例如医疗,领域数据量已经大到了形成“小社会”了。甚至口语,在医疗大数据中,也有属于医疗板块的社会媒体(例如 reddits 以及医疗问答之类)的存在,应该是并不缺乏数据的覆盖性。主要短板还是团队与团队不同,产出质量可能就不一样。



立委:分析大模型也有,BERT 就是,只不过风头现在被 GPT (暂时)碾压了而已。BERT 的微调曾经很风行,医学界也有一些经过微调的 BERT 模型在公域,可是效果不太好。

另外,我们理解的分析和生成也可能跟不上时代了,表面上看 next token 作为基石或原理的所谓自回归弄醒当然就是生成模型,道理上在分析任务上应该不适应,或至少不能与分析模型争锋:语言分析任务包括问句意图理解、阅读理解还有诗词创造(诗词创作不是生成任务,而是更加依仗全局的理解和布局)等。但事实上,当一个所谓的“生成”模型在建模的时候,可以记住足够长的 precontext 的时候,模型本身的分析能力,就与上下文两边都看的模型,没有实质性的短板了。

总之,实践和效果把生成模型推到了现在的高度,而且貌似成为迄今最接近 AGI 的一扇门。当然,谈“终结”还太早。



chat 虽然不好说终结了AI或NLP,但基本终结了聊天和对话。此前甚至以后的一切人机交互,要突破这个天花板是很难了。因为从语言层面几乎到了无可挑剔的程度,虽然从个体的不同偏好来看,还有很多可以挑刺的地方。至少是没留下足够的余地,使得后来者可以给人更大的惊喜。



立委:从一个角度看,“语言-认知”其实可以分开来看,语言已经搞定了,认知搞定了多少?我说过,认知根本没搞定,也就是 20% 的认知能力吧,但给人的印象却远远不止 20%。多数时候给人的感觉(或错觉)是,貌似它也搞定了认知,只是偶尔露怯而已。可是人类露怯也不是特别罕见的事儿呀。


立委:其实我对所谓创造性的人类独有论越来越持有怀疑。人类肯定有某种机器没有的东西,但创造性(的大部)感觉不在这个神圣的圈或点内。很多以前认为的创造性 譬如艺术创作 其实是比较容易被模仿甚至超越的了。现在看到大模型的生成物(AIGC),常常可以看到创造的火花。当然,我们总是可以 argue,所看到的AIGC 的创造性其实是我们的误读,或过度解读,是 Eliza effect,读出了对象本身不具有的意义和美感来。这个 argument 自然不错,但还是无助于界定“创造”的人机边界。例如 AIGC 刚刚“创造”的这幅命题作品:水彩画 爱情。




回看一下人类如何学到这些知识吧:经过小学5-6年,中学5-6年,大学4年,研究生5-10年,最后是不是研究学问的料还不知道。但除了这样漫长和精心设计的教育体系,人类还想不出任何其他更加多快好省的知识传承和突破的办法来。有些学问的点滴突破 已经到了需要一个人穷尽一辈子去消化前人的认知,才能站在历史的肩膀上在某一个点上,可能做出某种突破,来延伸科学知识的前进。而做出突破的都是幸运儿,因为一将功成万骨枯,他的脚下不知道有多少无法达到彼岸的半途而废者。拿这样的知识体系作为人类最后的神圣领地也许很有道理,因为掌握它是太难了。但隐隐觉得 AI 在这个过程中,可能有希望有所颠覆。颠覆不是 AI alone 而是 AI assist,原有的教育体系使得科学进步的 overhead 越来越大,大到了人类寿命与之不相称的程度。最终还是要诉诸 AI 来缩短这个过程。这方面 (AI for science)的方向也是值得关注的(大模型在生物工程方面据说就开始扮演一个加速器的角色了)。至于这个方向的进展对于人类科学的神圣性有什么影响,目前还不好说。也许科学的神圣和严谨也不是铁板一块的。




即便天气预报,人貌似搞定了,但也还是不得不借助类似于 LLM 的大模型大计算才搞定的。预测模型所做的事情,与人类所做的预测有什么根本区别吗?硬要看区别,可能主要还是人不如模型,人拍脑袋做决策 与(借助)模型做决策,差距只会越来越大,因为人太容易只见树木不见林了。人类掌控全局的能力其实是一个很大的短板。(据说林彪可能是一个例外,他的脑袋长得好。是个异数。)



立委:鸡同鸭讲啊。必需精分 bipolar 才好。

进一步说明了形式和内容可以分离,分离了也可以随机融合,融合了不 make sense 但看上去却很雄辩。









立委:胡说的判定因人而异,对人的背景有要求。而语言的判定,只要 native 基本就行。


立委:定义不清楚:什么叫知识?什么叫学到?什么叫“真正学到”?判定的标准是什么?如果标准是他的体温 脉搏和肾上腺素的分泌,是不是呼应了他的知识,那肯定是没学到。



立委:这个能力已经是漫山遍野了呀。知识从概念化起步,概念化的模型表现 已经是笃定的了。zero shot 的本义就在于此:你用概念 instruct 模型,模型可以从概念的“理解”,返回给你实例。



从“流畅的句子”(语言能力)到“真正的知识”,是不是存在鸿沟(是否可以逾越)呢?对人类而言,很多“知识”,载体就是“流畅的句子”。所以,给人一种错觉:流畅的句子 = 知识。我觉得这是ChatGPT给一般人的错觉。

有知识 → 能说流畅的句子 (这个合理)
能说流畅的句子 → 有知识 (这个存疑)





卫东:一个实数 > 一千多亿个参数?





某种意义上,序列符号形式的语言,就是上帝赐予的压平知识的天然工具。其结果是无限的冗余 啰嗦 重复。LLM 就是在这些冗余中学到了“知识”,重新表示/复原到多维空间去。到了生成阶段,又不得不再次降维,压平成串,照顾人类的感官(眼睛/耳朵)。














上面的回答 我的感觉是属于 一本正经的信息量较低营养不高的话。有信息量的部分就是提到了“习惯”。他无心,我有意,这些习惯表达法不就是约定俗成的习惯吗。符号绑定的用法,本源上就没有什么道理。






貌似还没学会。哈 没有这个知识,就好比它目前写中国诗没有学会押韵一样。但是英文诗是押韵的呀,也许就是一个阶段问题,还没进阶到那个段位:也许是等中国的大模型赶上来吧。



立委:照猫画虎 其实已经开始抽象规律了,否则就是 照猫画猫。






立委:以前符号那边的常识推理名家 cyc 老打比方,说:,去年我们是10岁孩子的常识推理能力,今年的目标是12岁。

类似的,LLM 的抽象能力它现在也许达到了大学生的能力,但还不到研究生的水平。就更谈不上达到专家 教授的高度抽象能力。但它走在万能教授的路上 @宋柔

宋柔:@wei 40后和60后有信仰差别[Grin]




A large language model for electronic health records

BioGPT: A Domain-Specific Generative Transformer Language Model Pre-Trained on Large-Scale Biomedical Literature

chatGPT 网址:需要注册)


李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:神迹与笑话齐飞,chatGPT 也是大观了》


立委:chatGPT 的面世,比尔盖茨认定是可与电脑的发明以及互联网的诞生一样级别的划时代事件,我认为是恰当的,因为人类迎来了人机交互的新时代。




LeCun 就是没明白这一点。尽管人家平时看上去不仅是大牛,而且也接地气。但这次他是选择性失明,小看了“对齐”导致的超级核弹效应。

哈,已经上亿的用户量,不怪他常常罢工。好在罢工的时候,还总是临时、现场给你唱个幽默自嘲的小曲儿啥的。(不会太久,随着多模态LLM时代的到来,这个 rap 就该可以现场演唱给你听。)

Li Chen:所以难的不是语言,而是人脑袋里怎么想的,怎么去理解的。即便是同样一句话,在不同场景里,也就是所谓的context,效果也不一样。而具体的context应该是无穷无尽的,再多的参数也cover不过来。

霄云:Right, but for service chatbot, this is not a problem. The number of actions that it can carry out is limited. 

So chatgpt essentially demonstrates conversational user interface for general public is good enough now. May not be good for professional domains without domain dependent model.

Li Chen:是的,现在这个chat才算是可用的chat,给普通人玩足够了。以前的真心就是3,5轮之后就不想在继续了。某种意义上说所谓的闲聊机器人应该没有太大的继续研究的价值了。

立委:@lc 说的对,chatGPT 之前的所有聊天系统,包括小冰,都没有真正做到,chatGPT 算是 “终结”了聊天。只有它才第一次以这样的规模和自然度,让它成为聊天的天花板。总还是可以挑剔的,众口难调。但挑剔的地方大多不过是一种不同偏好的折射。关键是,人机交流已经解决了。

chatGPT 碾压以前所有的聊天系统,是典型的降维打击。功夫在chat外,本质是搞定了人机接口:人类第一次见识了,面对机器,不需要编代码(或用什么咒语,例如所谓 prompt engineering),只要直接跟它说干嘛就行。它听得懂任何语言。聊天只是外壳和表象。它的存在远远大过聊天,但凡文字语言类任务,它无所不能。碾压聊天也只是其NLP泛化的AGI道路上的顺带副产品而已。

霄云:Now the only thing left is how to cheaply ground the understanding with easy to build interaction logic and service APIs . 





Minke:language is not mind


语言和思维的关系 记得在普通语言学课上 就是一个焦点问题,就好比鸡与蛋的问题一样,一直纠缠不清。纠缠不清的原因之一是 稍微像样一点 具有一些条理的思维,总是与语言裹在一起的,无法分离 。

直到1957年乔老爷提出句法要独立研究 不要被语义干扰 这才从理论上给出了一条把语言与思维剥离的可能性。但实际中的对象也还是混沌一片的,毕竟“绿色思想在睡觉”这样的思维实验的案例不是真实现象的常态。

直到今天遭遇了 chat’gpt …… 到了 chat 这种似人非人的生成物,这才第一次大批量地让我们见识了,什么叫形式与内容的貌合神离。原来语言还带这么玩的呀,一本正经不妨碍胡说八道。



语言是符号,以前以为,因此思维也必然是符号,现在没有以前那么确定了。也许思维的本真形态更靠近 向量空间,只是到了脱口而出的那一刻,才穿戴整齐变成了符号的形式:语音流 或 文字流 。


立委:符号学派一直是这样建模的:语言是表层符号,吃进去或吐出来的时候就是这样。消化在内,就成了深层符号,所谓 logical form 或者逻辑语义表示,tree 或 dag 就是它的形式化数据结构。以为这才是思维的真谛。现在开始动摇了,也许我们脑袋里的思维长得不是这个样子。只不过这个样子作为理论,比较方便我们理解自己,方便做符号形式的逻辑演算而已。而且建立表层符号与深层符号表示的映射比较直观,增强了可解释性。

Li Chen:这个有道理的,其实人类自己也不确定所谓的思维,意识到底是什么。只不过符号,语言这个东西更容易理解交流,然后人类的文明都在这个基础上发展而来,所以想从根本上不谈符号,不谈逻辑,又显得不太现实。


毛老:是啊 ,所以离开符号的AI 终究不会是完整的AI。不管它做得多么像模像样,终究还会“胡说八道”。既然是“一本正经的胡说八道”,就不能说已经通过了图灵测试。如果是一个人在“一本正经地胡说八道”,别人就会说:这个人钟点不准。十三点。

立委:问题是,一本正经,是人都可以判断。胡说八道则不然。判断其胡说八道,以及胡说八道的程度,因人而异。如果是专业领域的胡说八道,绝大多数老百姓是感觉不到的。非专业的胡说八道 其实各人的敏感度也不同。图灵测试规定了裁判的选择标准了吗?需要多少生活阅历 多少教育程度 才够格? 这是从裁判的角度。

从内容角度,胡说八道本身也有区别,有的胡说八道九成以上的人都可以轻易识别,也有的胡说八道(或“狡辩”)则需要专家中的精英才可以识破。还有一些似是而非或似非而是的灰色地带,说胡说也可以 但换个角度胡说却成了洞见。其实人类社会很多警句、禅悟,包括鸡汤,都离胡说不远。这是因为 就好像狂人日记一样,出格的、不同寻常的胡言乱语,往往暗藏玄机。

语言的问题,相对比较黑白分明,道地不道地, 找几个 native speakers 来,容易达成共识。内容的问题比较容易灰色很多是软约束。有些乍看是胡说的东西,往后退一步 或换个角度 又言之成理了。甚至 1+1=3,这种数学上纯粹的胡说,在场景中可能就是合理的语义表达。譬如,团队工作往往是一加一等于三,两个恋人结合也往往是一加一等于三:成了核心家庭。语言中这样说1+1=3,一点也不违和。前面把模型绕晕又得到模型认可的两个大苹果加四个小苹果等于八个小苹果也是如此。说到底这些都是层次纠缠,形式逻辑兜不住不同层次的搅合。可层次纠缠是客观存在的语言表现,因此让“胡说”与否的判断变得格外困难。加上内容层面的默认、脑补、覆盖等日常认知措施,都有因人不同的设定,事情就更加复杂。 狡辩/雄辩 在人类社会之所以普遍存在,也是因为很多内容表示具有两可的空间。最后一条,多数人都有过胡说八道的表现 有多有少,完全免疫极少。


可是回头看自然语言,在 LLM 面前,最多就是个小山丘。什么内递归,外递归,什么习惯用法,语义相谐,篇章远距离,计算风格,都不在话下。

那天跟 @李志飞 聊,他说他最惊诧的是,LLM 除了语言表现外,你让他 parse 出中间结构,它也能做得有模有样。中间结构本来就是内部的,但现在它也可以外露,进一步说明语言结构是搞定了。既然语言结构搞定了,逻辑搞定也是早晚的事儿,因为二者都是封闭集。搞不定的还是知识点,这个由于 80-20 的大数定律,没办法在有限的数据中穷尽它。结果就是真假混杂。




立委:FSA 干掉 CFG 有充分的理论依据,我在我的小书中阐述过,实践中也证实过。“小书”指的是:李维 郭进《自然语言处理答问》(商务印书馆 2020)

关键就是 deep 多层。神经正好就是 deep 和 多层。所以,我们符号这边多年的探索和创新,从架构上看,与深度学习是殊途同归。从这个角度,我们用 FSA 多层架构的符号办法搞定了自然语言的结构,与LLM的搞定,道理是相通的。

问题是 符号多层可以搞定结构,但搞不定鸡零狗碎的“语义搭配”,更搞不定计算风格。而 LLM 特别擅长这些鸡零狗碎。


立委:白老师说过的搞定语义相谐的力量对比,感觉其实调用的手段严格说不属于符号手段。再者,符号系统如果希望像chat那样搞定计算风格 例如写出莎士比亚,我持有怀疑的感觉。



立委:差不多。也可以说 deep强调的是有合适的表示空间,多层强调的是有足够的学习空间。前者是 tensor 或其他表示(例如符号这边的graphs),后者是过程。宏观上看,AI两条路线的走向是如此的平行,回头看,不这样就很难驯服自然语言这个 monster:

自然语言之所以被看成是大山,主要不是结构,更主要的是那些鸡零狗碎的搭配。里面参杂了种种规则与反规则的矛盾统一。可是现在回头看,那些鸡零狗碎也还是有限的,可以穷尽的或分级 generalize 的,不过就是需要的参数量足够大(或者在符号这边,足够多的符号特征以及分层的大小规则),是我们当年无法想象的参数量级。

尽管如此,面对知识(点)的海洋,billion 级别的参数搞定语言有余,但很多知识还是无法捕捉。前几天估算过,捕捉到的也不过就是 20% 左右吧,但给人的感觉却是80%上下。



LLM 搞定语言的最大功绩就是让我们借助它有了泰山登顶,有一种 “一览众山小” 的视野。但横在泰山前面的是真正的知识喜马拉雅山,包括各个领域的知识壁垒。








霄云:有效的人就那么几个。计算 存储 的增长速度 比人要大很多,如果核聚变能源变成现实。养一个太难了,他们估计还会用 llm produce。

video 能不能反哺文本有定论吗?也许计算增加后有可能。







霄云:I actually think chatgpt violated the maximum relevancy principle of conversation, even if it want to interpret this way, it should have clarified first. This is a subtle point, however.

梁焰:就是,要不然他总可以狡辩:“ more context is needed. ‘


霄云:Maybe their alignment is too strong.  有几个labelers 喜欢转牛角尖。

For service chatbot this is no good.  But then again it should compute implied meaning that can be grounded to actions, instead of literal meaning . 




霄云:Soft prior ,不是红脖子。



chatGPT 网址:需要注册)

It is untrue that Google SyntaxNet is the “world’s most


李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:chatGPT 帮你写应用文》

立委按:自从 chatGPT 横空出世,这类应用文辅助写作案例已经水漫金山,见奇不怪了。但这不改变辅助写作是一个具有巨大应用场景和价值的方向,虽然大概率与机器翻译也差不多,最后作为行业可能被做死,或成为白菜价。下面是我自己前一阵子做的一些试验,光顾玩,未能及时记录,摘录如下。


请帮助我用规范的英文写一篇推荐信,内容包括:1. 推荐张三到清华做博士后,研究AGI;2. 我是张三的博士导师; 3. 张三博士论文获到全国优秀论文大奖

Dear Admissions Committee,

I am writing to highly recommend Zhang San for admission to your PhD program in AGI research at Tsinghua University.

As Zhang San's doctoral advisor, I have had the pleasure of working with him for the past three years and have been consistently impressed by his intellectual curiosity and dedication to his research. His thesis on AGI has been recognized with the National Outstanding Thesis Award, a testament to the quality and originality of his work.

In addition to his impressive research abilities, Zhang San is a team player and has proven to be a valuable asset to our research group. He is able to work well with others and consistently contributes fresh ideas to our discussions. I am confident that he will thrive in a collaborative environment like your PhD program.

I strongly believe that Zhang San would be an excellent fit for your program and would make a valuable addition to your community of researchers. I wholeheartedly endorse his application and hope that you will consider him for admission.


[Your Name]


Dear Admissions Committee,

I am writing to highly recommend Zhang San for admission to your PhD program in AGI research at Tsinghua University.

As Zhang San's doctoral advisor, I have had the pleasure of working with him for the past three years and have been consistently impressed by his intellectual curiosity and dedication to his research. His thesis on AGI has been recognized with the National Outstanding Thesis Award, a testament to the quality and originality of his work. In particular, his contribution to the development of a novel AGI algorithm has garnered attention from researchers in the field and has the potential to significantly advance the state of the art.

In addition to his impressive research abilities, Zhang San is a team player and has proven to be a valuable asset to our research group. He is able to work well with others and consistently contributes fresh ideas to our discussions. I have seen him take the lead on several collaborative projects, always ensuring that all team members are on the same page and making valuable contributions to the final product. I am confident that he will thrive in a collaborative environment like your PhD program.

I strongly believe that Zhang San would be an excellent fit for your program and would make a valuable addition to your community of researchers. I wholeheartedly endorse his application and hope that you will consider him for admission.


[Your Name]

好了一些,但请增加下列事迹:1. 废寝忘食 他总是最后一个离开实验室 2 发表了5篇一级刊物论文,成为青年学术新星 3 虽然AGI被认为是难以实现目标,张三在定义AGI实现的不同步骤上提出了具有操作性的建议,并实现了10个步骤中的前5个

Dear Admissions Committee,

I am writing to highly recommend Zhang San for admission to your PhD program in AGI research at Tsinghua University.

As Zhang San's doctoral advisor, I have had the pleasure of working with him for the past three years and have been consistently impressed by his intellectual curiosity and dedication to his research. His thesis on AGI has been recognized with the National Outstanding Thesis Award, a testament to the quality and originality of his work. In particular, his contribution to the development of a novel AGI algorithm has garnered attention from researchers in the field and has the potential to significantly advance the state of the art. Zhang San has also published five papers in top-tier journals, establishing himself as a rising star in the field of AGI research.

Despite the challenges and setbacks that are inherent in AGI research, Zhang San has remained committed to his work and has often stayed in the lab long after everyone else has gone home. His dedication to his research has paid off, as he has already achieved significant progress in his work. In particular, he has proposed operational steps towards the realization of AGI, and has successfully implemented the first five steps of his proposed framework.

In addition to his impressive research abilities, Zhang San is a team player and has proven to be a valuable asset to our research group. He is able to work well with others and consistently contributes fresh ideas to ......

中国教授头疼的为学生写推荐信问题,已经完美解决。只要你能列出散乱 零星的要点 机器就会给你拽成一篇看上去非常 professional 的规范推荐信。有没有问题? 当然有。例如明明是要求博士后推荐,它写成了 to your PhD program,但关键是语言方面的困难都给你搞定了,只要通读一遍把个别词改正一下即可。


1. 中国封控一夜取消,180度大转弯 2 由此带来急剧的社会变化,包括大面积感染在各地同时蔓延,以及不同人对于这一波疫情的恐惧 3 一方面广大群众为重新获得自由而欢呼雀跃 另一方面也对目前疫情可能失控深感忧虑 4 专家前后不一致的说法 使得公众思想更加混乱 5 各种滥用药品的情况开始出现 6 政府无法对政策转变自圆其说 权威下降。










chatGPT 网址:需要注册)


李维 郭进《自然语言处理答问》(商务印书馆 2020)





人工智能当代史 outline:


5年前的第二次AI深度革命以NLP为发端,“老流氓”(LLM:large language model, BERT/GPT)为旗帜,辐射多模态,实现了AI从感知到认知的跃迁,靠的是原生大数据的自监督学习,无需标注却继承了有监督训练的巨大能力。

3个月前的第三次AI创新(或革命)搞定了自然语言的理解和生成难题,以 chatGPT 发布为里程碑,开辟了人机自然交流的新纪元。

通用人工智能(AGI:Artificial General Intelligence)因此从空想变为可以逼近的工程目标。

有个声称有百万多日本用户的新闻门户约了要采访我,basically 问老司机关于“老流氓”的现状和展望,尤其是 chat 对于社会的冲击力。有什么特别要给公众传达的?或者就信口开河 lol

想了一下,以上的 outline 算是我信口开河前的主线,尽量做到天马行空不逾矩吧。



立委:从 chat 的客户端感受,理解已经很给力了:其实我个人觉得,chatGPT 的理解能力比其生成能力更让我 impressed,也许是生成模型的生成表现在预料之中,而语言理解此前一直是一个让人头疼的坎儿。从终端用户角度,你不管以什么方式说话,不管说的是哪一门语言或哪几门外语, 貌似它都能听懂,给出的回应很到位,没有违和感。

关于AGI 以前与@马少平 老师等谈过n次,当时我们都认为是不靠谱的东西,乌托邦。可是最近一年来,我的立场动摇了,不知道马老师现在如何评价?


立委:👍 哈,heroes 所见略同,同步进退 lol


霄云:真正的no code


为民:计算机语言可能也会就像法律语言一样,作为人类自然语言的一个分支 ··· ChatGPT 开了一个头,试图统一服务接口: 基于自然语言的接口。


立委:理解上的突破还是很明显的。如果我们回顾功能对话(Siri,Alexa,Google Assistant,出门问问等)的问句理解,语言理解面的对比还是很强烈。Siri 类是 brute force,貌似理解了,人工斧痕明显,无法与大模型 “涌现” 出来的理解能力比,无论自然性、广度还是鲁棒。 当然,以前提过,功能对话的助理有很强的功利性和数据库的接口性,目前LLM在这方面如何对接还有挑战,但就理解谈理解,就对话谈对话,这种段位的差别还是非常有感的。功能助理是个机器,而chat已经进阶到类人实体了。

霄云:但是,这个理解是 人infer的,并没有接口可以用和检测。换句话说如果它一个坏的理解和一个配套坏的生产,我们并不知道。我们知道只有end to end。

立委:客户的体验是可以粗略度量的。可以计算多少次失望是因为“感觉”它没听懂。这个感觉,用过的人心里是有一本账的。chat 绝对优等。


立委:尼克有何高见?前面勾画的是“人工智能当代简史”。从范式转变的角度,十年内一大革命(深度神经),两大创新(transformer-based LLM,chatGPT)。考虑到 LLM 实质上还是有监督的学习(虽然由于其不需要带标数据常常号称是无监督),其有效和成功还是植根于深度神经网络的范式,所以也可以把第二次革命(LLM)降格为创新。但是,如果从第二次的感知飞跃到认知的角度,从第三次人类对齐(human alignment)赋能搞定人机接口、 摘取自然语言这颗人工智能的皇冠明珠的角度,三次革命论也站得住。也就是说AI最近10年间发生了三大技术革命,明显有加速度的态势,让人眼花缭乱。

这种发展速度已经酝酿了危机,因为人类实际上没有准备好应对以假乱真 真假莫辨的信息时代。AI 可以缓行,这并不是杞人忧天,可是,实际上谁也阻挡不住它的脚步和洗礼。


这叫真假莫辨。懂行的朋友说里面九成为真 一成胡说 。


人类社会如何应对 misinfo 的泛滥,目前还是个问号。


为民:@马少平 马老师春节期间写的人工智能简史科普非常不错,值得大家看。@wei 


少平:lu总说的是这个,刚写完还没有仔细校对,请多提意见。是为之前写的算法介绍写的一个“开篇”:开篇 什么是人工智能v1.0


尼克:那天白老师的分享挺好。我在琢磨,1)如何看待现在。需要一套话语来总结chatGPT现在的功能,或者说科普,现在很多人说的都不太准确。2)同时也要理解ta的局限,有些局限可能可以从计算理论角度说明,做GPT的人也需要了解。3)chatGPT可以外挂别的功能,别的模块也可以外挂chatGPT当oracle来用。4)哲学上,chatGPT帮助更加接近清晰定义“理解”,我现在对Chinese Room,维特根斯坦后期哲学和老乔的立场又有些新思路。我在等GPT4.0出来在决定是否增补个《人工智能简史》第三版。

少平:@Nick Zhang(尼克) 期待


尼克:哈哈,Schimdhuber对没给他发图灵奖很不满。前一阵写过一个很长的啰里啰唆的人工智能历史回顾。人工智能300年! 这个是改写的中文版,原文在这里。

为民:这些可能说明人工智能是工程应用学科,没人管自己重新发明的工程技巧之前是否别人做过,谁最接近应用成功,Credit 就是谁的[Grin]




少平:@Nick Zhang(尼克) 那个神经网络先驱奖如何?以前不太知道,这次写东西查资料才看到。

尼克:那奖还不错,我印象那奖原来是Neural Networks Society的,后来不知为啥转到IEEE名下了。2010年前的得奖人,我都八过。基本是个安慰奖。刚设立时恰逢Hopfield革命。


立委: @Nick Zhang(尼克) 无论gpt老四是个什么怪物 你的简史都必须更新版本 ,才跟得上时代。



尼克:可不,历史就是 in between焚书坑儒 and 独尊儒术。


过去10年的黄金时代 让人眼花缭乱 但有些事可以盖棺论定的感觉。

尚未搞定的短板自然还在路上。我戏言过的 AI 裁为三截 一截馈符号 一截赠领域 一截还科学。

没有自然语言符号的原生数据这源源不断的燃料,llm 如何练成,成为多模态认知引擎。领域这一关必须过,看各路神仙今后五年吧。至于科学,不是说AI都是摸着石头过河,模型创新走马灯似的,有人说多是撞了狗屎运,以至于搞出的东西自己都没想到怎么那么牛。想不通又解释不了,就说“涌现”,上升到量变质变的玄学高度。


最近浏览多模态的一些工作,说音频视频方面的基础大模型正在重复 llm 的成功 ,说明 llm 的多任务零样本路线是ai的有普适意义的突破性方向。做音频视频的人,特别嫉妒自然语言的特性,觉得自然语言的 llm 条件太好了,不怪这场革命五年前首先发生在 nlp。第一是自然语言词汇符号的离散性和有限性,边界清晰,是嵌入向量的理想对象。第二是自然语言的结构性,以前被认为是线性模型的拦路虎,但在装备了多头注意力机制的深层llm中,也变成了理想的建模对象。于是 llm 轻松搞定了 有限的词汇表示无穷的变体这门人类绝活。

如果是上帝垂怜人类,为人类创造了语言符号系统作为交流工具和知识 containers,同样应该感谢上帝的语言符号创造,为AI的llm认知革命提供了理想的原材料或燃料。而音频视频这些连续模态,没法与 text 比出身,根不红苗不正,起点不一样,既不离散也没有语言那样的结构化。尽管如此,过去一两年他们还是在基础模型方面克服困难,进步很大。因为从数据学习概念,是铁律。



chatGPT 网址:需要注册)


李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:chatGPT 辅导精读学术论文的实验》

做过几个实验,把 chatGPT 当成tutor,帮助我精读论文,看它有多少胡说,有多少循循善诱深入浅出。

当然,对我不懂的专业,胡说我也看不出来,只有专家能看出来。说一下初步体验,chatGPT当辅导员是一个很好的方向,几乎不需要做太多的工作,只要能接上就可以根据不同客户的目标朝向任何专门辅导的方向去发展,例如:(1)辅导面试;(2)辅导论文阅读;(3)辅导编程序(已经有 co-pilot 就是做那个方向,最近那个从特斯拉离职以前负责特斯拉自动驾驶的牛人现身说法,他说他现在写程序 70% 以上借助 co-pilot,虽然也听说过其他 guru 对 co-pilot 不屑一顾,说我懂的我不需要它,我感觉痛点的地方它帮不了我。这些不同的反应很正常,因为并不是每一个人都需要和拥抱“被机器辅导”的新潮流,就像有人有自动驾驶却从来不用,而我现在离开自动驾驶感觉非常别扭一样);(4)………


只能看 pros and cons 的权衡了。只要牢记不能拿它的话当成金科玉律,保持适当的警惕性,对于关键环节需要多方做求证才能应用,那么被辅导也就不是问题,可以让辅导的利益最大化。这与使用 FSD 开车的感觉类似,你不可以全信这个 beta 系统(即便将来取消 beta 标签,也不能全信),它的确可能在最糟糕的情景下做出最灾难性的选择,你必须保持警惕,随时干预和接管。但是从中受益的人还是很多。美国市场的特斯拉车主渗透力达到了1/4强,记得是二三十万人每天在用它。你不能说这几十万人都是傻瓜:花费一万多美金心甘情愿做小白鼠,冒着自己的生命危险,为马斯克趟雷,给特斯拉免费做标注和反馈。还是被-autopilot 所带来的好处远远大于它的潜在危害,才会有 early adopters 愿意去拥抱技术。同理,用 LLM 帮助做辅导一定会带来很多好处,虽然它可能会误导。

辅导精读论文这事儿,chat 可算是比较拿手,不管什么专业它都能。谁叫它好为人师。辅导的过程大体如下:我跟他说,请你帮助我精读下面这篇自然论文,我需要对它有一个深入的理解,来应对考试。然后让它做摘要,同时用中文和英文,然后一步步问它各种问题。感觉它回答问题主要还是针对给他的目标文章,但因为它此前这个领域的文章也看了不知道多少(虽然最近两年的没看),所以你问它的任何问题,它都可以解释回答。这与教授辅导学生也一样,教授也是用自己脑袋里的领域知识,不仅仅是所面对的目标论文,来做讲解。

以上算是铺垫,我们现在就现场直播一场辅导,目标论文就是名满天下的那一篇AI论文,发明 自注意力机制 transformer 的那个 attention is all we need,看它能不能比一般人科普、辅导得更加深入浅出。为什么选这一篇做实验,因为 transformer 这个框架和 attention 这个机制,是我们现在见到的这些LLM奇迹的技术源头。其中 transformer 的工程优势也是一个重要因素,但这不是我关心的点,所以没有在下面的 tutoring 过程中强调。这个实验还可以继续向深处做下去,让它讲解每一个术语和细节,看它最后是不是还靠谱。这需要调动积累的知识,它比起一般的老师应该更有记忆。


reading comprehension and summarize this important research in simple terms in both English and Chinese so every college student understand:

Attention Is All You Need Abstract The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 1 Introduction Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15]. Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [21] and conditional computation [32], while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains. Attention mechanisms have become an integral part of compelling sequence modeling and transduc- tion models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases [27], however, such attention mechanisms are used in conjunction with a recurrent network. In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 2 Background The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU [16], ByteNet [18] and ConvS2S [9], all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [12]. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2. Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22]. End-to-end memory networks are based on a recurrent attention mechanism instead of sequence- aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34]. To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence- aligned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9].

The research paper “Attention Is All You Need” proposes a new network architecture called the Transformer, which is based solely on attention mechanisms and does not use recurrence or convolutions. The Transformer is able to perform better on machine translation tasks and requires less time to train. It also shows that the Transformer can be applied to other tasks, such as English constituency parsing.

研究论文 “Attention Is All You Need” 提出了一种新的网络架构,称为Transformer,它完全基于注意机制,不使用循环或卷积。Transformer能够在机器翻译任务上表现更好,并且训练时间更短。它还表明Transformer可以应用于其他任务,如英语选区解析。

The Transformer architecture uses an attention mechanism to allow the model to draw global dependencies between input and output sequences, without being constrained by the distance between the positions. This allows for more parallelization during training and results in a new state-of-the-art in translation quality. Additionally, the Transformer uses Multi-Head Attention to counteract the reduced effective resolution caused by averaging attention-weighted positions. Overall, this architecture allows for more efficient and effective training of sequence transduction models.

Transformer架构使用注意机制,使模型能够在输入和输出序列之间建立全局依赖关系,而不受位置之间距离的限制。这允许在训练期间进行更多的并行化,并导致翻译质量的新技术水平。此外,Transformer使用Multi-Head Attention来抵消由平均注意力加权位置引起的有效分辨率降低。总体而言,这种架构允许更有效率和有效的序列转换模型训练。

Wei: what is new and what is different, compared with previous studies:

The main difference between the Transformer architecture and previous studies is that it does not use recurrence or convolutions. Instead, it relies solely on an attention mechanism to draw global dependencies between input and output sequences. This allows for more parallelization during training, and results in a new state-of-the-art in translation quality. Additionally, the Transformer uses Multi-Head Attention to counteract the reduced effective resolution caused by averaging attention-weighted positions. Previous studies have used attention mechanisms in conjunction with recurrent networks, while the Transformer architecture completely eliminates the use of recurrence. This makes the Transformer more efficient and effective for training sequence transduction models.
Transformer架构与先前的研究的主要区别在于它不使用循环或卷积。相反,它完全依赖于注意机制来在输入和输出序列之间建立全局依赖关系。这允许在训练期间进行更多的并行化,并导致翻译质量的新技术水平。此外,Transformer使用Multi-Head Attention来抵消由平均注意力加权位置引起的有效分辨率降低。先前的研究在循环网络中使用注意机制,而Transformer架构完全消除了对循环的使用。这使得Transformer更有效率和有效的用于训练序列转换模型。
Wei: what is attention mechanism? how does it work?


An attention mechanism is a method used in neural networks to selectively focus on certain parts of the input while processing it. The attention mechanism allows the model to weigh the importance of different parts of the input and focus more on the parts that are more relevant to the task at hand.

In the case of the Transformer architecture, the attention mechanism is used to draw global dependencies between the input and output sequences. The attention mechanism works by computing attention scores for each position in the input sequence, and using these scores to weight the importance of each position when generating the output sequence.

The attention mechanism can be thought of as a function that takes in a query, a key, and a value, and returns a weighted sum of the values, where the weighting is determined by the similarity between the query and the key. In the Transformer architecture, the query, key and value are all represented by the same set of learned parameters, this is called self-attention. The attention mechanism can also be extended to multiple heads, where multiple sets of query, key, value are learned and then concatenated to form the final output, this is called Multi-Head Attention.

In summary, attention mechanism is a way of allowing a neural network to focus on certain parts of the input, and weigh the importance of different parts of the input when generating the output. This improves the ability of the model to learn dependencies between input and output sequences, regardless of their distance in the sequences.

Wei: explain it in simple terms in Chinese so everyone can understand




总之,注意力机制是允许神经网络关注输入的某些部分的方法,并在生成输出时重要性评估输入的不同部分。这提高了模型在输入和输出序列之间学习依赖关系的能力,而不受序列中位置之间的距离的影响。简单来说, 注意力机制是一种让神经网络在处理数据时能够重点关注重要部分的方法,这样可以使网络更好的学习到数据之间的关系。

Wei: why multi-head?


The Multi-Head Attention in the Transformer architecture is used to counteract the reduced effective resolution caused by averaging attention-weighted positions. With multi-head attention, multiple sets of attention weights are learned, and then concatenated together to form the final output.

By using multiple attention heads, the model can attend to different parts of the input sequence and learn different representations. These different representations can then be combined together to form a more comprehensive understanding of the input sequence. The multiple heads also allow the model to learn more complex relationships between the input and output sequences.

Additionally, Multi-Head Attention allows the model to attend to different parts of the input sequence at the same time. In the case of the Transformer, the attention mechanism is applied to the entire input sequence, which allows the model to draw global dependencies between the input and output sequences. With Multi-Head Attention, the model can attend to different parts of the input sequence in parallel, which further improves the ability of the model to learn dependencies between input and output sequences.

Therefore Multi-Head Attention is employed to enhance the capability of the model to take into account multiple representations and multiple dependencies simultaneously, and also to deal with the reduced effective resolution caused by averaging attention-weighted positions, this way the Transformer can achieve better performance on the tasks it was designed for.

Wei: explain this explanation in simple terms in Chinese so college students can understand 




因此简单来说, 多头注意力机制是用来提升模型同时考虑多种表示和多种依赖关系的能力, 以及解决平均注意力位置导致的有效分辨率降低的问题的, 这样Transformer才能在其设计的任务上取得更好的性能

Wei: 这样做难道不会组合爆炸?


使用多个头的注意力机制可能会增加模型的复杂性,并增加计算成本。然而,研究表明,在Transformer架构中使用多个注意头能够显著提高模型的性能。在Transformer中,使用多头注意力机制的方法是将多个头的输出拼接在一起, 并不是每个都做组合,这样的方式可以有效的减少模型的复杂性和计算成本。

Wei: 还是不太理解为什么要多头。本来的注意力机制就已经可以在任意两个tokens





可是,就我们以前的研究来看,两个tokens之间一般只发生一种本质的关系,其他关系最多就是桥梁,是达到深层本质关系之前的表层关系而已。例如,被动语态的动词 V 与句法主语 N 最终会转化为谓语与逻辑宾语的关系。这样的话,并不真地需要多头的复杂性。

您说的是对的,在某些情况下,多头注意力机制可能不是必要的。 因为在某些任务中,只需要考虑两个token之间的主要关系即可。 但是,在Transformer 模型中, 多头注意力机制是为了更好的学习全局依赖关系,并且在大量的实验中,这种模型结构表现出了较高的翻译质量。



多头是指序列中的每一个 token 都会延伸为多个变体的向量表示(头),然后与其他token的多个变体的向量表示(头)两两计算关系的权重,是这样吗?


简单来说,Transformer模型是一种基于注意力机制的编码器-解码器架构。它由一系列相同的层组成,每一层都由一个多头自我注意机制和一个简单的全连接前馈网络组成。 其中编码器会将输入序列映射到一个连续的表示上,解码器则会根据这个表示来生成输出序列。 多头注意力机制是通过计算输入序列中每一个token的多个变体与其他token的多个变体之间的关系权重来学习输入序列的不同部分之间的关系。

Wei: 编码器的输入输出是什么,解码器的输入输出是什么?

The Transformer model is a neural sequence transduction model that uses an encoder-decoder structure. The encoder maps an input sequence of symbols to a sequence of continuous representations, and the decoder generates an output sequence of symbols one element at a time using auto-regression. The model is composed of stacked self-attention and point-wise, fully connected layers for both the encoder and decoder. The encoder is made up of N=6 identical layers, each with two sub-layers: a multi-head self-attention mechanism and a simple, position-wise fully connected feed-forward network. The decoder also has N=6 identical layers, but includes an additional sub-layer that performs multi-head attention over the output of the encoder stack. Attention functions are used to map a query and a set of key-value pairs to an output, where the output is computed as a weighted sum of the values using a compatibility function. The Transformer model uses Scaled Dot-Product Attention, which is similar to dot-product attention, but includes a scaling factor to improve performance for large values of dk.
Wei: explain in simple terms in Chinese:
Transformer 模型是一种神经序列转换模型,它使用编码器-解码器结构。编码器将输入符号序列映射到连续表示序列,解码器则逐个元素生成输出符号序列,使用自回归。模型由堆叠的自注意力和点连接层组成,分别用于编码器和解码器。编码器由 N=6 层相同的层组成,每层有两个子层:多头自注意机制和简单的位置连接前馈网络。解码器也有 N=6 层相同的层,但包括一个额外的子层,用于在编码器堆栈输出上执行多头注意力。注意力函数用于将查询和一组键值对映射到输出,其中输出是使用兼容函数计算的值的加权和。Transformer 模型使用缩放点积注意力,类似于点积注意力,但包括一个缩放因子来提高大 dk 值的性能。 
Wei: 什么是自回归?




Wei: 全联接层中的 point-wise 是个什么鬼?


“point-wise” 指的是全连接层中的点积操作。

在神经网络中,全连接层(fully connected layer)是一种常见的层类型,它将输入的每个元素与权重进行点积,并使用一个非线性激活函数转换结果。

“point-wise” 这个词描述的是全连接层中对于每个元素单独进行点积操作,而非对于整个序列进行点积操作。

在 Transformer 模型中,编码器和解码器均使用了 point-wise 全连接层,这些层被用于将输入序列映射到连续表示序列。

Wei: 前馈网络与全联接层是相同的吗,如果不同,二者的关系是?


前馈网络 (feedforward network) 和全连接层 (fully connected layer) 是不同的概念,但是它们可以组合在一起使用。


在 Transformer 模型中,编码器和解码器都使用了前馈网络结构,其中包含了多个全连接层,用于将输入序列映射到连续表示序列。


Wei: 编码器与解码器是如何对接的?


编码器和解码器是在 Transformer 模型中相互独立但协作的两部分。编码器将输入序列映射到连续表示序列,而解码器则使用这个连续表示序列来生成输出序列。


具体来说,在解码器中,第三个子层执行多头注意力,它会将编码器堆栈输出作为其键值对(keys and values),而将解码器堆栈输出作为查询(queries)这样解码器就能够充分利用编码器预处理出来的信息来生成输出序列。这样编码器和解码器就可以相互协作完成序列的转换。

Wei: 为什么编码器解码器都是六层模型?

在 Transformer 模型中,编码器和解码器都是由六层构成的。这个选择是为了在模型的复杂度和精度之间取得平衡。

具体来说,Transformer 模型中编码器和解码器使用了多层注意力机制,每层都能够捕获不同级别的语言信息。这样更深层的信息能够更好地表示更高级别的语言结构,而浅层的信息能够更好地表示基础的语言结构。使用六层模型就能够在一定程度上捕获到不同级别的语言信息,这样能够更好地处理文本。


总之,使用六层编码器和解码器是在 Transformer 模型中得到较好结果的一种做法,但是其他层数也可能同样有效。

Wei: 可以增加解码器层次,减少解码器层次吗?

是可以的。在 Transformer 模型中,编码器和解码器的层数可以根据需要进行调整。





Transformer 模型之所以成为 AI 的通用结构,是因为它提供了一种高效的方法来处理序列数据,而序列数据在很多 AI 任务中都是常见的。

首先,Transformer 模型使用了多头注意力机制,这使得它能够自动学习序列数据中的依赖关系。这对于解决许多 NLP 任务,如机器翻译、语音识别等都是非常重要的。

其次,Transformer 模型使用了并行化的计算方式,这使得它能够高效地处理长序列数据。这对于解决许多 NLP 任务,如文本生成,文本分类等都是非常重要的。

此外,Transformer 模型的设计也使得它更容易被训练和优化,模型参数也更容易被预训练,这对于解决大量 NLP 任务是非常有利的。

总之,Transformer 模型提供了一种高效、灵活、易训练的方法来处理序列数据,这使得它在许多 AI 任务中都能取得良好的结果,成为了一种通用的架构。

Wei: 其实它已经用于一切数据的模态了,不仅仅是序列数据,也不局限于NLP

您是对的,Transformer 模型不仅仅是应用于 NLP 任务,它也被广泛应用于视频识别、图像分类、语音识别等其他任务中。这是因为 Transformer 模型提供了一种高效的方法来处理序列数据和对齐不同元素之间的关系,而这些都是很多 AI 任务中都需要处理的。例如在视频识别中,Transformer模型可以通过自注意力机制来学习视频帧之间的关系,来识别动作。在图像分类中,Transformer模型可以通过自注意力机制来学习图像中不同区域之间的关系,来分类图像。

所以说,Transformer 模型的架构和思想对于解决许多 AI 任务都是有效的,这也是它能够成为通用结构的原因。

Wei: 图像不是序列数据,为什么一样有效?



例如,在图像分类中,我们可以将图像转换为一个序列,比如将图像划分为若干个网格,然后将每个网格的像素值看作一个序列中的元素,然后使用 Transformer 模型来处理这个序列。

同样在视频识别中,我们可以将视频转换为一个序列,比如将视频按照时间顺序将每一帧看作一个元素,然后使用 Transformer 模型来处理这个序列,来识别视频中的动作。

这样做的好处是,Transformer 模型可以通过注意力机制来学习序列中元素之间的关系,来解决许多 AI 任务。

总之,尽管原始数据不是序列数据,我们仍然可以将其转换为序列数据来使用 Transformer 模型进行处理。



Q: What is the Transformer?  
A: The Transformer is a neural sequence transduction model that has an encoder-decoder structure. The encoder maps an input sequence of symbols to a sequence of continuous representations, and the decoder generates an output sequence of symbols one element at a time.

Q: How does the Transformer work?  
A: The Transformer uses stacked self-attention and point-wise, fully connected layers for both the encoder and decoder. The encoder is composed of a stack of identical layers, each with two sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. The decoder is also composed of a stack of identical layers, with an additional sub-layer that performs multi-head attention over the output of the encoder stack.

Q: What is the attention function used in the Transformer?  
A: The attention function used in the Transformer is called "Scaled Dot-Product Attention", which is computed using dot products between queries, keys, and values, and a softmax function to obtain the weights on the values.   

Q: How does the Transformer differ from other neural sequence transduction models?  
A: The Transformer differs from other neural sequence transduction models in its use of stacked self-attention and point-wise, fully connected layers for both the encoder and decoder. Additionally, the Transformer uses the Scaled Dot-Product Attention function for computing attention, which is different from other attention functions like additive attention.

Q: Can you explain the Encoder and Decoder Stacks in Transformer? 
A: The Encoder is composed of a stack of N=6 identical layers, each with two sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. The encoder uses residual connections and layer normalization to improve performance. The decoder is also composed of a stack of N=6 identical layers, with an additional sub-layer that performs multi-head attention over the output of the encoder stack. The decoder also uses residual connections and layer normalization, and also has a mechanism to prevent positions from attending to subsequent positions. 

Q: What is the purpose of residual connections in the Transformer? 
A: The purpose of residual connections in the Transformer is to improve the training of the model by allowing the gradients to flow more easily through the layers. This makes it easier for the model to learn the identity function, or the identity mapping of the inputs to the outputs. Additionally, Residual connections help to mitigate the problem of vanishing gradients, which is a common problem in deep neural networks.
Q: What is the purpose of Multi-head attention in Transformer?  
A: The purpose of multi-head attention in Transformer is to increase the representational capacity of the model. Instead of having a single attention mechanism, multi-head attention allows the model to attend to different parts of the input sequence at the same time. This allows the model to extract different types of information from the input sequence, which can be useful for different types of tasks.

Q: How does Multi-head attention work in Transformer?  
A: Multi-head attention works by splitting the queries, keys, and values into multiple heads, and then performing the attention function on each head separately. The outputs from all the heads are then concatenated and transformed by a final linear layer to produce the final output. By having multiple heads, the model is able to attend to different parts of the input sequence simultaneously and extract different types of information.

Q: What is the Layer normalization used in the Transformer?  
A: Layer normalization is a technique used in the Transformer to improve the stability and the performance of the model during training. It normalizes the activations of a layer by subtracting the mean and dividing by the standard deviation of the activations. This helps to prevent the activations from becoming too large or too small, which can cause problems during training. It also helps to speed up the convergence of the model.
 Wei: continue in Chinese


Q: Transformer 和其他神经序列转换模型有什么不同?  
A: Transformer 与其他神经序列转换模型不同在于它使用了堆叠起来的自注意力和全连接层来实现编码器和解码器。此外,Transformer 使用了缩放点积注意力函数来计算注意力,这与其他注意力函数(如加法注意力)不同。 

Q: 在 Transformer 中,编码器和解码器堆栈有什么作用? 
A: 编码器由 N=6 个相同层组成,每层有两个子层:多头自注意机制和位置敏感全连接前馈网络。编码器使用残差连接和层归一化来提高性能。解码器也由 N=6 个相同层组成,除了两个子层外,解码器还有一个子层,它在编码器堆栈输出上执行多头注意。解码器也使用残差连接和层归一化,并且还具有防止位置注意后续位置的机制。

Q: Transformer 中残差连接的目的是什么? 
A: Transformer 中残差连接的目的是通过使梯度更容易流动来提高模型的训练。这使得模型更容易学习输入和输出之间的单位函数(即输入和输出之间的同一映射)。另外,残差连接还有助于缓解深度神经网络中常见的梯度消失问题。 

Q: Transformer 中的缩放点积注意力函数是如何工作的? 
A: 缩放点积注意力函数是 Transformer 中用来计算注意力的方法。它通过计算查询、键和值之间的点积,并使用softmax函数来获得值的权重。它是在一组同时的查询上计算注意力函数,将它们打包到一个矩阵Q中。键和值也被打包到矩阵K和V中。我们计算输出矩阵如下: Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) * V 




立委:我们只知道在出现一种可以把任何东西encode,变成(decode)任何其他东西的变形金刚(transformer)架构发明以后 大数据大算力驱动的“老流氓”(llm: large language model)就开始搞怪。也才几年?委员mao说,搅得周天寒彻:

而今我谓llm 不怕这高 不怕这多雪 安得倚天抽宝剑 把它裁为三截 一截馈符号 一截赠领域 一截还科学 太平世界 环球从此狂热。


立委:哈,有些体验是出圈到做梦似的,让人怀疑现实与幻觉的界限何在。在我的职业生涯中,考上MT硕士后,第一次在语言所导师的办公室见到电脑(那还是在前PC时代),有一种震撼,搞不清这个怪物在闪烁着绿色光点的屏幕上搞什么鬼。绕过统计的噩梦做了N多年的符号NLP,终于有机会在工业界把 parsing 做到顶级水平,感觉离珠峰只有一步之遥,写过一篇《美梦成真》记述这种幻觉似的成就,好像巴别塔就要在我手中建成了,梦见自己成了上帝。第三次就是神经浪潮的连续轰炸,直到这次亲眼见到货真价实的巴别塔在LLM手中的建成。巴别塔之后,还有什么妖呢?好奇多于狂热。

我的NLP博文汇编成的那本书《巴别塔影:符号自然语言处理之旅》延宕很久,估计今年应该出版了吧。与其说是科普,不如说是故事。塔影塔影,过眼烟云,雁过留声。NLP/MT 对我是刻进血液的惘然,想起最爱吟诵的李商隐的诗:


不仅仅当时惘然(or 枉然 lol),而是惘然了一辈子。

为民:郭德纲说流氓会武术,谁也挡不住; 立委老师是语言学家会浪漫,谁也挡不住



白硕:@wei 哈,啥啥皆空。


立委:AI 这样下去,真地是要推着我们由色生情,自情悟空了。盒子已经打开,空还会远吗?

不过,我至少还是不相信 LLM 能自主写出这样的千古绝句来的。人类一定还有一个点,是模型翻越不过去的坎儿。



立委:玩下来,觉得中文效果超出预期。当然是英语表现最强。决定于数据量。日常的会话各种语言之间感觉不到大的差异。但要他写诗,出来的英文诗歌更像回事,也押韵。而中文诗就没学会韵脚,更谈不上格律,与@孙茂松 老师的《九歌》和华为的诗词,差太远了。


立委:这个他们论文中提到了,token 是决定于频率的,如果词素或词素组合到了一个坎儿,就被作为一个 token。


立委:不知道,如果 GPT4 里面中文数据按比例增加3个量级,把所有能见到的中国古典诗词全部一网打尽,是不是就突然学会了,可以媲美《九歌》呢?

其实现在这个势头能缓慢一点才好,但谁也无法阻止人类实体的竞争冲动。社会确实没有准备好。misinfo 的挑战会搅乱正常的社会秩序。





建强:看到刘老师这个例子来了兴趣 让写了个transformer model的诗 也押韵 且言之有物。

立委:不错不错。@TaoHong 诗人是不是有些发怵了?

读呆Limerick&商籁诗。// sonnet












为民: 👈 这个是 OpenAI 的产品副总裁的一个访谈,透露了 OpenAI 的爆款产品研发和商用背后的一些有趣信息。中文编译版(上、下两部分)👇



描述的是一种第一次体验到超出设计者预期的量变引起质变时候的感受。crazy 但它 happened。在并不是很多的案例中找到了翻译的映射,然后神奇地推广到任意的翻译,这就是zeroshot 的实质:它突然懂得了“翻译”的意义。所谓听懂人话,就是在一系列关键指令的概念层次,它建立了相应的推广映射。给人感觉就是贴心文秘 说什么它做什么。可机制却是这么简单,就是在能够容纳足够大的前文的时候,预测 next token。这么简单的机制,当它用于大到我们不敢想象的规模数据的时候,智能“涌现”的奇迹就发生了。


令人惊讶的是,这就是从感觉它相当愚笨到可以与之互动的原因,像 GPT-2 有点的酷炫,但大多数时候也感觉它非常愚蠢,我认为在GPT-3中,它有时会表现得出乎意料的好。不要误解我的意思,GPT-3仍然会犯很多愚蠢的错误,但在某些任务上,它可能有30-50%的时间是正确的,有时甚至更好。就好像突然之间在你需要抽样和尝试任务之前,也许每隔20次你就会看到一次,“哦,这个看起来不错”。有了GPT-3,它开始每三次发生一次,或每两次,或每五次发生一次,你会说,“哦,天哪,这实际上是……”

这是 gpt3 发布不久他们的分享,记叙了当时的惊喜:生成品大约每三到五次会有一个结果令人满意。

为民:这个过程惊喜不断 ··· “这几乎就像一个人,现在我们有一个头脑风暴的合作伙伴,你可以把它应用到所有这些不同的任务上。我觉得非常酷的是,我发现很多公司都在利用这一点,创造你以前做不到的新体验。我认为这是非常令人兴奋的。我觉得回答问题也非常酷,但是这个问题出乎我的意料。我认为我们不会预料到这是一个如此大的用例。”

立委:与人类偏好对齐后的 chat GPT,平均一到两次,就出现一个可以接受甚至惊喜的结果。这种体验对于普罗特别重要,普罗要的是一锤子,最多两锤 ,事不过三。因此虽然 chat 的潜力全部蕴藏在 gpt3 中 ,没有chat,也还是藏在深山人未识 。chat 所谓的与人类偏好对齐,原理也极简单:不过就是把人类对生成的满意不满意反馈给模型。谁想到这么一来,这家伙任劳任怨之上又学会了说话的乖巧 。这个强化对齐的工作效果这么好,也是预先难以想象的。不要小瞧这第二次的对齐工作的意义。正是因为在对齐方面晚了一步,meta 发布了类似模型几天后就不得不匆忙收回 ,谷歌以及其他一两家大厂也一直迟疑,不敢亮剑,只能内部研究。

2 到 3 是圈内见证奇迹,3 到 3.5 的 chat,是圈外见证奇迹。不知道小4又会出什么幺蛾子。技术爆发加速度的阵势,看热闹都感觉累得喘气,邪门。这种见证NLP奇迹的惊喜两次发生在同一个团队、同一批狂人身上,也是奇了,好比 open AI 两次被雷劈中。

顺便一提,chatGPT 主要不是 chat,那就太小看了它的第三次创新浪潮的意义了。chatGPT 不过是借用了 chatbot 那张皮,真正实现了人机无缝通讯(所谓人机自然语言接口)。聊天(chat)只是它在人机交互的时候可以听命于人去做的无数任务中的一项。chatGPT 之前的所有人机交互的努力,全部是解放前的感觉,到 chatGPT 才真正解放。虽然总是可以找到它并不理解的案例(人何尝不也是如此,有谁可以理解任何场景的任何对话),但无法否认它给人的感觉就是可以向它任意发布指令(chatGPT 的先行姐妹模型就叫 instructGPT),无论用什么语言。它很鲁棒,也不在乎你的口误。


立委:我的第一感觉是:openAI 在这方面做了手脚。“对不起,我只是一个模型,我不懂 blah blah“,太像模版话术了,一点也不自然。我认为这是它与人类偏好对齐中的败笔,但可以理解他们这么做的苦衷:毕竟,任何担责的风险必须规避,也不想刺激人类中某些人的小心脏。





志飞:如果有预设固定分类的话 那gpt就一点都不神奇了,那我鄙视openai 论文从来都不说,属于造假。



立委:机器如何知道自己什么是看过了而且消化了(懂了),什么是看过但没消化(没有找到 pattern,没有上升到概念),什么是连看也没看过呢?如果真知道自己不懂,“一本正经的胡说八道”就可以杜绝。所以它说不知道就是个外部强加的托辞(通过问题分类或其他手脚),绝不是它知道自己不知道。“不懂”的情形非常像是话术,常常打断了交流的自然性和顺畅性,让人扫兴。更多是因为政治正确压力加入的。“一本正经胡说八道”则是模型的内在本性,是生成模型中形式与内容脱节的表现。语言模型学会了语言(形式),学得非常完美,但并不可能学会语言所能表达的所有知识。知识那东西是罩不住的,再大的数据也罩不住。理想状态是形式等于内容,语言很精当,内容也正确。在知识缺乏的时候,形式就大于内容,胡说八道不可避免。形式小于内容的情况不会出现,因为前面说了,语言大模型已经把语言(形式)搞定了,一般不会出现有内容难以表达的情况。但相反的情况很难避免,目前看来大约有10%-20%的时候,形式大于内容。于是信口开河的无中生有、张冠李戴都会出现。

暴力美学的结果是把 99% 的实体(人或团队)排除在外了。大家(包括非常聪明的 talents)基本都是在戏台底下看戏。第二个结果是,对于多部门团队资源和财力资源的依赖增强了。

为民:所以 LLM 可以在各个方面至少都显示人类能力的平均水平。




1. 先是符号AI靠边站,成为资料员;
2. 接着是搞 feature engineering 的精算师靠边站,让给神经了;
3. 接着是搞 finetune 的人靠边站,让给 prompt engineers (few shots);
4. 现在是 prompt engineers 靠边站,让位给自然语言指令了 (zero shot)。玩AI门槛无限低。因为是人都会发号施令,哪怕以前从来没有人听。

这个 AI简史 看上去既让人兴奋 也让人后怕。

为民:··· 然后 ··· 人类就退化了。









看下来,LaMDA 其实与 chat(chatGPT) 是一个量级的存在。只不过 chat 汲取了它的教训,用一种非自然的方式口口声声声称自己没有感情、更没有意识,只是一个机器(模型)。如果与人类偏好对齐工作中不做这种不自然的强加,从对话的角度看,二者是非常类似的。说 chatGPT 这方面不自然,是因为它刻意与人类保持距离,而它的说话方式与人类如此接近,这二者之间是有根本矛盾的。





























可是,人类的小心灵比较脆弱,为了怕吓着了部分终端人类客户,模型推出前有必要避免这个麻烦:于是 chatGPT 不得不过分地切割。个人感觉这一点是个败笔。

为民:欢迎爱丁堡大学的高才生👏,下面雄文的作者符尧同学@符尧 。见:“拆解追溯 GPT-3.5 各项能力的起源 How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources

立委:欢迎👏 你这一篇解剖麻雀 群里热议过 很细致精彩 @符尧。有一个疑问 想听听你的看法 chatGPT 在遇到敏感话题 例如涉及宗教 政治 人机关系 机器人格化 回应的时候 总是过分谦虚 并回避 很像是外加的分类与模版话术的手脚。


立委:I guess that is the case, between the two, some inital problems handled by patterns will be transfered over time to the alignment training.  patterns are a quick way of enforcing some policy.



立委:遇到状况,还是写 patterns 比较容易 handle 吧,对齐来不及照顾周全,除非是对齐的标注工作流程化到了那种程度,任何状况可以迅速启动 并且确保那种状况可以及时更新系统和应对。特别是紧急状况,不小心放出了有毒内容。

符尧:他们有一个 filter 会拒绝一些东西。然后模型会再拒绝一些东西。有两层。

立委:making sense。这就是我们以前常说的,定点纠错能力,还是符号 patterns 最灵活有效。当然也不妨碍后去为了合适泛化这些 patterns,要把这些临时的pattern和词典工作转移到训练去。

玩常了,能察觉到有些拒绝非常不自然,后面也常跟着翻来覆去重复自己身份、刻意与人类切割的话术(“我只是一个语言模型,我不懂感情….” 等等)。

fliter 这些都是工程操作的常态。launch 一个面对几百万客户的服务,这些都是合理的部署。

为民:另外,对这样的服务,除了这两层,某种形式的 Human-in-the-Loop 可能也必要吧?

立委:patterns 本身就是 human in the loop 虽然不是在线的 in the loop,因为 patterns 和 词典类的更新并不牵涉训练和发布周期,是随时可以进行的。必须有专人盯着前线,一个“毒”放出去了或被侦察到,不能等到它酿成网络风暴,就要及时打补丁熄灭它,让其他蜂拥而上的人不能复现“毒品”,这是很重要的。

为民:另外,ChatGPT通过 prompt 的加强积累的学习能力是否可以迁移给其他用户, 时间或场景?@符尧

ChatGPT 对这个记者的 Dating 帮助不大[Grin] “ I asked ChatGPT to reply to my Hinge matches. No one responded.

因为 Prompt 不会改变模型 …

立委:prompt 或 NL 可改变人设,触发/调动的是模型内部已经有的人设 patterns。




Great! Let's start with your research experience. Can you tell me about your previous research projects and what you have worked on?

I am new graduate from SUNY Buffalo, specialized in Information Extraction in medicine domain from big data of clinical records. I just got my PhD and defended my thesis last month.

Congratulations on completing your PhD! Can you tell me more about your research in information extraction from clinical records? What were some of the challenges you faced and what were your main findings or contributions?

Before I present my PhD research, I would like to point out my research background. I was intending to do medicine after my bachelor's program in statistics. Somehow I did not manage to get into medical college, instead I entered the PhD program of NLP and IE. So I was discussing with my supervisor and expressed my interest in carrying on domain research in medicine using NLP and big model.

I see, that sounds like an interesting background and research direction. It's great that you were able to combine your interest in medicine with your PhD studies in NLP and IE. Can you tell me more about your specific research project in information extraction from clinical records? What were the main challenges you faced and what were your main contributions or findings?






李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:zero shot 抽取、摘要、吟诗、阅读理解》

这是昨天的新闻,讲的是美国共和党遭遇议长选举的闹剧和挑战。送进去,我让它给我抽取:(1) 实体;(2)引语;(3)关系;(4)事件。用的是大白话,这些以前都是 NLP 的专项任务,现在 zero shot 它,给个指令即可。

这个回答显示了其NLP的超能力。传统的sentiment analysis 不过就是一个正负二级分类。在这里不大合适,而它会把带有情绪的 insights 抓出来,显得非常灵活精当。


这里说明一下:它其实是已经开始翻译成阿拉伯语,出来了一些阿拉伯语的古怪文字从右向左蹦。走到一半的时候有个罕见的bug,出了 load failed 错,结果我重新 submit 前面的指令,它就接着开始翻译日语和土耳其语。



谁说不会创作,让它改写成诗,就执行了。那些 “对不起,我只是程序,我不会创造” 这种套话,其实是与人类对齐的时候附加上的,压抑了它的潜力和天性:



不但把新闻写成了讽刺小品,而且还解释了哪里是讽刺的语气。而且也学会一点“知之为知之不知为不知”的谦逊美德。给 chatGPT 评一个语文二级教授,如何?

为民:I Asked ChatGPT to Invent an Alien Language – This Is What Happened—-a49517e4c30b—4

立委:这个很 fun,造词方式有些像 Esperanto。书面 documentation 很全的人造语方案出现过150多种,估计也被chatGPT消化了。

为民:GPT-3的一些让人意想不到的潜力渐渐被挖掘出来。OpenAI 的工程态度和能力值得赞赏 [ThumbsUp] 这些不断被挖掘出来的能力 OpenAI 他们自己也没有想到:”I don’t think we would have predicted that one being such a big use case.”

立委:意想不到的能力发掘的根本原因是听懂了人话。在此之前,NLP 社区为了定义一个任务,需要耗费很多的精力去设计目标,准备数据和统一标注标准,组织社区竞赛。这就必然限定了任务的种类。现在每个人都可以尝试让它做各种任务,人民群众的创造力得到了极大的发挥。以前太多的死角,现在都可以去尝试。

茂松:@wei 放一篇中文进去,试试效果怎么样?抱歉,我不会用。





chatGPT 网址:需要注册)

《AI浪潮:玩不死的 chatGPT (之二)》


李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:玩不死的 chatGPT》





















Title: A Neural Network Approach to Automatic Text Summarization


Automatic text summarization is a challenging task in natural language processing (NLP) that involves generating a concise and coherent summary of a given text. In this paper, we present a neural network-based approach to this problem. Our model is inspired by classic theories of linguistics, such as the Chomskyan notion of universal grammar, and combines them with the latest ideas in self-supervised pre-training of large neural networks. We argue that a universal grammar can be perfectly captured by a massive model, and that grammar and semantics can be separated. One key aspect of our approach is the use of a high-dimensional vector space to represent the meaning of language, and another is the use of multiple layers of neural networks to perfectly capture the phenomena of language. Finally, we demonstrate that our theory overcomes the limitations of massive models, such as "hand-wavy" speculations, by providing a clear direction for future research.


Automatic text summarization is a crucial task in NLP that has numerous real-world applications, such as news aggregation, document summarization, and social media analysis. The goal of text summarization is to generate a concise and coherent summary of a given text that preserves its main points and convey its meaning. However, this task is challenging due to the complexity and diversity of natural language, as well as the potential differences between the summary and the original text in terms of length, structure, and content.

There have been many approaches to text summarization in the past, ranging from rule-based methods to machine learning-based approaches. In recent years, deep learning-based methods have shown great promise in this domain, due to their ability to capture the rich and hierarchical structure of language. In this paper, we propose a neural network-based approach to text summarization that is inspired by classic theories of linguistics, such as the Chomskyan notion of universal grammar, and combines them with the latest ideas in self-supervised pre-training of large neural networks.


Our model is based on the idea that a universal grammar can be perfectly captured by a massive model, and that grammar and semantics can be separated. To this end, we use a high-dimensional vector space to represent the meaning of language, and multiple layers of neural networks to capture the phenomena of language.

One key aspect of our approach is the use of a high-dimensional vector space to represent the meaning of language. We argue that this is the optimal carrier for language meaning, as it allows us to capture the subtle nuances and relationships between words and concepts. To construct the vector space, we use a variety of techniques, including word embeddings, co-occurrence matrices, and distributed representations.

Another important aspect of our approach is the use of multiple layers of neural networks to capture the phenomena of language. We use a combination of feedforward and recurrent layers, as well as attention mechanisms, to capture the complex and dynamic nature of language. These layers are trained using a self-supervised pre-training approach, which allows us to learn rich and meaningful representations of language from large amounts of unannotated data.

Results and Discussion:

We evaluated our model on a number of benchmarks for text summarization, including the DUC and TAC datasets. Our results show that our model performs significantly better than previous state-of-the-art approaches, both in terms of ROUGE scores and human evaluation.

One interesting finding is that our model is able to capture the key points and meaning of the original text while still maintaining a concise and coherent summary. This is in contrast to previous models that
often generated summaries that were either too long or too short, or that contained irrelevant or redundant information.

One limitation of our model is its reliance on a massive model, which can be computationally expensive to train and deploy. However, we believe that the benefits of such a model outweigh the costs, as it allows us to capture a much richer and more nuanced understanding of language.


In this paper, we presented a neural network-based approach to text summarization that combines classic theories of linguistics with the latest ideas in self-supervised pre-training of large neural networks. Our results show that our model is able to generate concise and coherent summaries of text while preserving its key points and meaning. Our approach has the potential to greatly improve the efficiency and effectiveness of various NLP tasks that involve text summarization.

可惜,没有 references,发不了 ACL,至少格式上就过不了关。

add references in the proper places of this paper and add the list of references at end in the proper format as defined by top academic conferences:

Sure, I’d be happy to help you add references to your paper and create a list of references at the end. Here is an updated version of your paper with references added in APA style:

Title: A Neural Network Approach to Automatic Text Summarization


Automatic text summarization is a challenging task in natural language processing (NLP) that involves generating a concise and coherent summary of a given text (Sun et al., 2019). In this paper, we present a neural network-based approach to this problem. Our model is inspired by classic theories of linguistics, such as the Chomskyan notion of universal grammar (Chomsky, 1957), and combines them with the latest ideas in self-supervised pre-training of large neural networks (GPT-3, 2021). We argue that a universal grammar can be perfectly captured by a massive model (Yang et al., 2020), and that grammar and semantics can be separated (Firth, 1957). One key aspect of our approach is the use of a high-dimensional vector space to represent the meaning of language (Mikolov et al., 2013), and another is the use of multiple layers of neural networks to perfectly capture the phenomena of language (Hochreiter & Schmidhuber, 1997). Finally, we demonstrate that our theory overcomes the limitations of massive models, such as “hand-wavy” speculations (Lee et al., 2020), by providing a clear direction for future research.


Automatic text summarization is a crucial task in NLP that has numerous real-world applications, such as news aggregation, document summarization, and social media analysis (El-Kishky et al., 2020). The goal of text summarization is to generate a concise and coherent summary of a given text that preserve its main points and convey its meaning (Nenkova & McKeown, 2012). However, this task is challenging due to the complexity and diversity of natural language (Brown et al., 1993), as well as the potential differences between the summary and the original text in terms of length, structure, and content (Li et al., 2010).

There have been many approaches to text summarization in the past, ranging from rule-based methods (Mani & Maybury, 1999) to machine learning-based approaches (McDonald & Pereira, 2006). In recent years, deep learning-based methods have shown great promise in this domain (Huang et al., 2015), due to their ability to capture the rich and hierarchical structure of language (Zhou et al., 2016). In this paper, we propose a neural network-based approach to text summarization that is inspired by classic theories of linguistics, such as the Chomskyan notion of universal grammar (Chomsky, 1957), and combines them with the latest ideas in self-supervised pre-training of large neural networks (GPT-3, 2021).


Our model is based on the idea that a universal grammar can be perfectly captured by a massive model (Yang et al., 2020), and that grammar and semantics can be separated (Firth, 1957). To this end, we use a high-dimensional vector space to represent the meaning of language (Mikolov et al., 2013), and multiple layers of neural networks to capture the phenomena of language (Hochreiter & Schmidhuber, 1997).

One key aspect of our approach is the use of a high-dimensional vector space to represent the meaning of language. We argue that this is the optimal carrier for language meaning, as it allows us to capture the subtle nuances and relationships between words and concepts (Mikolov et al., 2013). To construct the vector space, we use a variety of techniques, including word embeddings (Mikolov et al., 2013), co-occurrence matrices (Pennington et al., 2014), and distributed representations (Hinton et al., 1986).

Another important aspect of our approach is the use of multiple layers of neural networks to capture the phenomena of language. We use a combination of feedforward and recurrent layers (Bengio et al., 1994), as well as attention mechanisms (Bahdanau et al., 2014), to capture the complex and dynamic nature of language. These layers are trained using a self-supervised pre-training approach (GPT-3, 2021), which allows us to learn rich and meaningful representations of language from large amounts of unannotated data (Devlin et al., 2018).

Results and Discussion:

We evaluated our model on a number of benchmarks for text summarization, including the DUC (DUC, 2002) and TAC (TAC, 2011) datasets. Our results show that our model performs significantly better than previous state-of-the-art approaches (e.g., Chen et al., 2018), both in terms of ROUGE scores (Lin, 2004) and human evaluation (Zhang et al., 2019).

One interesting finding is that our model is able to capture the key points and meaning of the original text while still maintaining a concise and coherent summary. This is in contrast to previous models that often generated summaries that were either too long or too short, or that contained irrelevant or redundant information (El-Kishky et al., 2020).

One limitation of our model is its reliance on a massive amount of data for training. While this allows us to learn rich and meaningful representations of language, it also means that our model may not be practical for use in resource-limited settings (Yang et al., 2020). Another limitation is that our approach is still limited by the quality and diversity of the training data (Bengio et al., 2013).


In this paper, we have presented a neural network-based approach to automatic text summarization that is inspired by classic theories of linguistics and combines them with the latest ideas in self-supervised pre-training of large neural networks. Our results show that our model is able to generate concise and coherent summaries that accurately capture the key points and meaning of the original text. While our approach has some limitations, it provides a promising direction for future research in this field.


Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (1994). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137-1155.

Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263-311.

Chen, Z., Zong, C., & Xiong, D. (2018). Fast abstractive summarization with reinforce-selected sentence rewriting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (pp. 1659-1669).

Chomsky, N. (1957). Syntactic structures. The Hague: Mouton.

Devlin, J, Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

DUC (2002). DUC 2002 overview. In M. Palmer, D. Gildea, & P. Kingsbury (Eds.), The second message understanding conference: A bridging event (pp. 1-19).

El-Kishky, A., Zhang, H., Wang, H., & Zong, C. (2020). Multi-view attention for abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4237-4247).

Firth, J. R. (1957). A synopsis of linguistic theory 1930-1955. In F. Smith & G. K. Zipf (Eds.), Current trends in linguistics (Vol. 1, pp. 181-258). The Hague: Mouton.

GPT-3 (2021). GPT-3: The third generation of the general purpose transformer. OpenAI.

Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 77-109). Cambridge, MA: MIT Press.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

Huang, P., Chen, D., & Liu, B. (2015). An overview of text summarization techniques. In D. D. Liu (Ed.), Encyclopedia of big data (pp. 1-9). Berlin: Springer.

Li, J., Liu, Y., & McKeown, K. R. (2010). The role of structure in text summarization. In A. F. Smeaton & P. Over (Eds.), Advances in information retrieval (pp. 1-18). Berlin: Springer.

Lin, C. Y. (2004). ROUGE: A package for automatic evaluation of summaries. In Text summarization branches out: Proceedings of the ACL-04 workshop (pp. 74-81). Stroudsburg, PA: Association for Computational Linguistics.

Mani, I., & Maybury, M. T. (1999). Advances in automatic text summarization. Cambridge, MA: MIT Press.

McDonald, R., & Pereira, F. (2006). Online learning of approximations to context-free grammars for natural language processing. Machine Learning, 65(1), 71-110.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111-3119).

Nenkova, A., & McKeown, K. R. (2012). An overview of automated text summarization. In Handbook of natural language processing (2nd ed., pp. 687-705). Boca Raton, FL: CRC Press.

Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (pp. 1532-1543).

Sun, X., Yu, M., & Duan, N. (2019). A survey on neural abstractive text summarization. arXiv preprint arXiv:1906.04165.

TAC (2011). TAC 2011 overview. In D. Vilar, J. E. Gonzalez-Brenes, & R. Prasad (Eds.), Text analysis conference (pp. 1-14).

Yang, Y., Le, Q. V., Salakhutdinov, R. R., & Xiong, C. (2020). The pre-training of deep bidirectional transformers for language understanding. In M. Botschen, A. Zündorf, & B. König-Ries (Eds.), Advances in artificial intelligence (pp. 11-19). Berlin: Springer.

Zhang, H., El-Kishky, A., Wang, H., & Zong, C. (2019). Improving abstractive summarization by data augmentation and fine-tuning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3382-3392).

Zhou, G., Liu, Y., & Cheng, X. (2016). A review of methods for text summarization. In Y. Liu & M. T. Maybury (Eds.), Advances in text summarization (pp. 1-18). Cambridge, MA: MIT Press.

不错,screening 这一关估计没问题了。接着练。




write a poem about it:

In the realm of computational linguistics,
Our team has made a great achievement.
A neural network-based approach
To automatic text summarization.

We've blended the old with the new,
Linguistic theories and self-supervised pre-training,
To create summaries that are clear and true,
Preserving the essence and the feeling.

This breakthrough will bring great rewards
To our company and all our supporters,
Improving efficiency and effectiveness
In language tasks that require text summarization.

We're filled with excitement and ambition
For this pioneering technology
To push the boundaries and bring new solutions
To the world of computational linguistics.

作为诗歌 不上档次,但拿它当作初创公司老总在公司聚会上的即兴感言,感觉还是基本合体的。




make it into a science fiction story or movie:

Sure! Here is a revised version of the science fiction story with added details and characters:

In the distant future, humanity had finally succeeded in creating a neural network capable of automatic text summarization. Our team of scientists, led by the brilliant Dr. Ava Park, had spent years studying the ancient art of linguistic theory, carefully blending it with the latest self-supervised pre-training techniques.

The result was a breakthrough that would change the world. Summaries that were clear and true, yet still preserved the essence and feeling of the original text. It was a technological marvel that promised to bring great rewards to our company, Transcendent Technologies, and all our supporters.

But we were not content to rest on our laurels. We knew that this was only the beginning. With excitement and ambition, we pushed the boundaries of this pioneering technology, bringing new solutions to the field of computational linguistics. The possibilities were endless, and we were determined to explore them all.

As we continued to make progress, we drew the attention of other tech companies and even governments. Many sought to acquire our revolutionary technology, but Dr. Park and the rest of the team were determined to keep it in the hands of Transcendent Technologies.

However, not everyone saw the potential for good in our work. A shadowy organization, known only as the Syndicate, began to threaten our team and attempt to steal our technology. It was a dangerous game, but we were determined to protect our breakthrough and use it for the betterment of humanity.

With the help of a skilled security team and some unexpected allies, we fought to keep the Syndicate at bay and continue our work. And as the years passed, we knew that our neural network would change the world in ways we could never have imagined.

into Chinese and add more details including Chinese scientists, technology and traditional oriental wisdom into the story, with background in a Forbidden City command center:

Certainly! Here is a revised version of the science fiction story set in China and incorporating Chinese scientists, technology, and traditional oriental wisdom:







好了,总结一下这次漫长的玩模型的旅程:起点是给一个科技发明的主题,然后就开始以此来考验 chatGPT 各种语言能力。测试点包括:1. 多语言能力(10种语言,外加洋泾浜);2. 多风格能力(学术论文、专利、法律文书、新闻、故事、莎士比亚文体等);3. 内容扩展能力(科幻)。

满分100分,得分95,另加意外惊喜奖励分20,它取得的总成绩是115。扣除的5分是它诗歌方面差强人意。意外惊喜包括:(i)科幻的创造能力(融汇背景指令、原科技内容与地球流浪并不生硬),(ii) 论文、专利和法律文书的格式做到了几乎不加修改即可利用,如果增加一些实质内容即可交付;(iii) 多语言能力无缝转接。




chatGPT 网址:需要注册)

《AI浪潮:玩不死的 chatGPT (之一)》


李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:关于chatGPT 的 思维链能力 》

立委:chain-of-thought (CoT,思维链)也可以翻译为不掉链子。这个能力我们在玩 chatGPT 的时候常常能体会到。相比于以前的模型,它不掉链子的表现令人印象深刻。

为民:我觉得 Google 的 LaMDA 可能也不弱,特别是他们的 Chain of Thought 技术, 看看他们是否可以像当时的搜索一样后来居上。但 OpenAI 目前占有先机: OpenAI 通过 DALL Es, GPTs, 特别是现在的 ChatGPT 积累了大量的用户使用数据以及从微软获得的代码数据(GitHub) (我之前问Yao Fu可能选择去 OpenAI 的原因,他提到其数据优势)。

志飞:chain of thoughts没啥具体技术吧?我记得就是prompt时加了个咒语 lets think step by step? 有没有具体技术论文。

为民:我之前怀疑 OpenAI 的初步chain of thought 能力使用了Google 的技术 , 但现在看来是由于其用代码训练的结果; 另外从Google 发布的结果来看,其语音模型的数学推理能力要比 ChatGPT 好一些。

之前发过这个: GPT 缺乏基本的推理能力(包括这种多步算术推理)。Google 用思维链有些帮助:”In ‘Chain of Thought Prompting Elicits Reasoning in Large Language Models,’ we explore a prompting method for improving the reasoning abilities of language models. Called chain of thought prompting, this method enables models to decompose multi-step problems into intermediate steps. With chain of thought prompting, language models of sufficient scale (~100B parameters) can solve complex reasoning problems that are not solvable with standard prompting methods”。

关键怎样激发 Chain of Thought 的能力。这个需要在模型训练时就build in。

刘群:最早提出的CoT是用少量样例(few shot)来激发的,后来有人提出只用lets think step by step,并把这种做法成为Zero-shot CoT。CoT应该是模型大到一定程度才能出现的能力。


志飞:为啥有COT能力?我有个假设:1. 模型看了大量的代码,学习了怎么一步一步“逻辑思考”;2. 模型看到了少量的咒语(比如说lets think by step)以及跟随咒语后的逻辑推理例子,学会了怎么激活逻辑推理;3. 通过这个,模型把咒语激发(只需少量例子)和具体怎么逻辑推理(需要海量例子)解耦了。





what is CoT (Chain-of-Thought)? How does an LLM acquire this ability? What role does it play in complex reasoning and in enhancing long conversation flows.

I'm not familiar with the term "CoT" or "Chain-of-Thought." It is possible that you may be thinking of a different concept. Could you provide more context or clarify what you are asking about?

It is claimed that large language model has learned this ability of chain-of-thought, which help the response to a complex problem to be step by step and more logical. I am just wondering how this can be learned

I see. It is possible that the term "Chain-of-Thought" is being used to refer to the ability of a language model to generate coherent and logical text that follows a chain of ideas or arguments.
Language models are trained on large amounts of text data and are able to learn patterns of language usage and structure. This enables them to generate text that is coherent and follows the conventions of written or spoken language.
In terms of complex reasoning, language models may not necessarily be able to reason in the same way that a human can, but they can generate text that appears to be logical and follows a chain of ideas. This can be useful for tasks such as generating responses to questions or creating natural-sounding text in a conversation. However, it is important to note that language models do not have the same kind of understanding or general knowledge as a human and should not be used to make decisions or solve problems that require critical thinking or judgment.

elaborate ...

Certainly. A language model is a machine learning model that is trained to predict the likelihood of a sequence of words. This is done by analyzing a large amount of text data and learning the statistical patterns and structures that are characteristic of the language. Once trained, a language model can generate new text by predicting the next word in a sequence based on the words that come before it.
One way that a language model can learn to generate text that follows a chain of ideas or arguments is by being trained on data that contains examples of such text. For example, if the model is trained on a dataset of news articles, it may learn to generate text that follows a logical structure, such as an introduction, a body with supporting details, and a conclusion. Similarly, if the model is trained on a dataset of academic papers, it may learn to generate text that presents a research question, describes the methods used to investigate it, presents the results, and discusses the implications of the findings.
While language models can generate text that appears to be logical and coherent, it is important to note that they do not have the same kind of understanding or general knowledge as a human. They are simply following the patterns and structures that they have learned from the training data and do not have the ability to think or reason in the same way that a human can.

good. learning by examples, that sounds like few shots learning for the chain of thought. Can it be learned by zero shot following natural language instruction such as "decompose it step by step"

It is possible that a language model could be trained to generate text that follows a chain of ideas or arguments in a step-by-step manner by being provided with examples of such text along with explicit instructions or labels indicating the structure of the text. For example, if the model is trained on a dataset that includes text that has been annotated with labels indicating the beginning and end of each step in a process, it may learn to generate text that follows a step-by-step structure.
It is also possible that a language model could be trained to generate text that follows a chain of ideas or arguments by being provided with more general instructions or prompts, such as "decompose it step by step." However, this would likely require a more complex model and more sophisticated training method, such as reinforcement learning, where the model is trained to maximize a reward signal by taking actions that produce the desired output.
It is important to note that while a language model may be able to generate text that follows a logical structure or chain of ideas, it does not have the same kind of understanding or general knowledge as a human and should not be used to make decisions or solve problems that require critical thinking or judgment.

关于 few shots,它的回答是: One way that a language model can learn to generate text that follows a chain of ideas or arguments is by being trained on data that contains examples of such text. 后面给了例子。

关于 zero shot,它的回答更绝:It is also possible that a language model could be trained to generate text that follows a chain of ideas or arguments by being provided with more general instructions or prompts, such as “decompose it step by step.” However, this would likely require a more complex model and more sophisticated training method, such as reinforcement learning, where the model is trained to maximize a reward signal by taking actions that produce the desired output.

虽然说,几乎可以肯定它并不是知道内部 know-how 试图透露机密的人/机,它的回答也是根据公开信息,预料之中的标准答案,但它的 chain of one argument after another, each followed by its own explanation or illustration,几乎滴水不漏啊。CoT, flowing, isn’t it amazing

这个例子还有一个有意思的点:开始的时候,它根本就不知道这个术语,处于无知状态,说明后来的回答是从我开始解释这个术语的时候,它才 in-context 知晓主题,并能“拼凑”出合适的看上去很有条理的回应。




为民:这个应该是合理的解释@李志飞 。不过如果需要通过CoT Prompt 激活,确实很局限,而且推理并不保证对。@魯東東

立委:context拉长(比如说4096个字)肯定是有助于学到这种长链条的 discourse 的路数的。很难想象以前的 context cut off 太窄,可以容纳这类学习。模型大,有助于这种 emerging/amazing 能力的孕育。

深刻怀疑做 chatGTP 的人 也在迷惑中 …. 所以大家伙儿也只好猜谜了。

志飞:我们昨天统一思想了:只把大模型能力归结为涌现是偷懒行为,我们要追根问底。我们要学习think step by step, 否则连GPT都不如了

立委:在下自叹弗如,诚恳地。无论是讲演还是回答问题,根本就没它那种条理性,只有一条“强过”它:比它富有激情,经常自己把自己 carried away。

为民:ChatGPT 在多个方面应该是超过人类的平均水平。所以对于个人来说,应该是大面积被它超越。





李维 郭进《自然语言处理答问》(商务印书馆 2020)



《AI浪潮:n-shot 是训练还是指令》

【立委按:NLP技术大喷发,以 chatGPT 为里程碑,石破天惊,让人目不暇给。社媒上圈内圈外,奔走相告,简直就是AI全民狂欢节的气氛。在这个当口,《AI浪潮》立委微信群组有幸请来诸多大咖,高谈阔论,蓬荜生辉。挂一漏万,拣有趣主题略记之,权作科普,以飨同好。】



为民:目前有关大模型的名词中文翻译有什么共识吗?比如“zero-shot”, “one-shot”, “few-shot”,是否用 零下,一下,几下?

立委:shot 是注射的意思,跟疫苗差不多。零针,一针,好几针。或者,零剂,一剂,好几剂。

东东:shot as in give it a shot?


为民:这个是比较形象,注入数据实例。但还是难 relate 。。。

立委:类似的东西以前有过 example-based, instance-based, boot-strapping, cold start 等等,属于弱监督学习模式,都是为了克服需要大量带标数据的知识瓶颈。但第二波深度学习革命(LLM 基础大模型 BERT/GPT等)以来,shot 开始流行起来,意思也产生了变化。

其中 zero shot 最有意思。乍一听,一针都不打,那不就是无监督学习吗?无监督怎么可能成事呢?最多也只是聚聚类而已。模型都不知道你要干啥,那还不是随机胡乱生成垃圾啊。

这个心结其实只是最近才解开,特别是到了第三波深度学习革命(以 GPT3 的playground 为代表,尤其是一个多月前发布的 chatGPT 作为里程碑),对于我们DL圈外人一切才豁然开朗。因为直到最近,模型才真正“学会了”人话,从而解决了“如何告诉模型你想要什么”的问题。

NLP任务以前只有两条路,第一是通过标注,通过实例,进行模型训练。第二就是用户或者产品经理用人话把自己的需求告诉开发者,开发者将其转化为工程的 specs,然后写程序或规则 hard-code 来实现任务。

现在不同了,对NLP多任务神器chatGPT,只要直接用人话告诉模型,它就直接实现,立等可取。也只有到现在,在通过所谓RLHF(人类反馈的强化学习)建立起来人机的自然语言接口后,zero shot 成为听令办事的好助手, 与无监督学习完全不同。

zero shot 不是无监督,而是监督不通过实例,而通过指令。指令前不久还是要用提示词(关键词咒语)技巧,chat GPT 让它进步到完全的自然语言。表达力丰富了不知道多少,门槛降低到0:是人都会说人话吧,都可以表达自己的需求。

例如实体识别任务。few-shots 就是给一排实例:“张三,李四,王二麻子,川普,普京,马斯克……”。1-shot就是打一针实例的剂量:“张三”,给个示范。到了 zero-shot,一剂量的样板都不给,只要直接下命令就好了:请把人名给我找出来。见下图:

另外,0/1/n shot 严格说不是 learning,因为传统的 learning 就是 training,是模型训练或模型微调的问题,属于线下的工作,只有模型完工之后 才会上线执行任务。而 0/1/n shot 是直接在线上执行相应的任务,没有训练什么事。

以前即便是弱监督的微调,也是要有一批数据(通常也要好几百实例吧,而 few-shots 通常是几个,最多几十个),为了在基础模型上做目标定向或修正。到了提出 n-shot 说法和做法的时候,默认就是立竿见影,在线就要完成指定任务。LLM 本身是不变应万变的。




李维 郭进《自然语言处理答问》(商务印书馆 2020)



随笔:AGI 迷思与反思

这两天在琢磨一件事儿。从AIGC(AI Generated Content)琢磨AGI(所谓 Artificial General Intelligence)。

其实直到不久前,对于 AGI 一直有点嗤之以鼻。主要是这所谓的通用人工智能 其实没有个像样的定义。我就觉得是扯淡,是科技界的共产主义大饼。当然小编和媒体是从不缺席的,各种鼓吹从来不缺乏,但感觉从业人员如果心心念念 AGI,有招摇撞骗之嫌。

半年多来,准确地说是自从开始玩GPT-3开始,逐渐反思这事儿,觉得 AGI 并不是不可以论,至少比共产主义靠谱得多。


但是沿着那个思路走,再回头看自从预训练大模型(BERT/GPT等)横空出世以来的AI表现,AI 的确是在通向越来越通用的金光大道上。

回顾历史,AI 过去的成功几乎全部是专项的成功。最早的源头是特定的机器翻译和极窄的专家系统。到了统计年代,也是场景味道特别浓厚:因为数据都是场景的,领域越受限,AI效果越好。虽然算法有共用的部分,但系统和模型都是专项的,因为数据都是场景的,领域越受限,AI效果越好。这也从AI社区的任务划分上看得出来。拿 NLP 来说,翻译、问答、聊天、摘要、阅读理解、辅助写作(校对/诗歌/对联)等等,都是各自一个门类。岂止是NLP应用的各种任务的分类:MT, summarization, writing assistant, grammaer checking, chatbot, question answering, reading comprehension, poem generation, etc,甚至 NLP 内部的很多事儿,也都是各自有自己的任务和社区、竞赛等等:named entity, relation extraction, event extraction, text classification, parsing, generation, sentiment analysis, topic analysis, etc. 这种情形一直持续很久,以至于第一线做实际工作的人,一听说AGI高调,就很不屑。


通用不仅仅表现在 NLP 天下归一,更表现在多模态AI的飞速发展,同样的基础模型+下游的机理,类似的 transformer (台湾教授翻译成“变形金刚”)架构,在所有的信号任务上,无论是文字、声音/音乐还是图片/美术、视屏,也都通用了。

预训练以前的时代,AI 深度神经革命(10年前)是从图片刮到了音频再到文字,根本解决了带标大数据的监督训练通用问题。但很多很多场景,带标大数据是匮乏的,这个知识瓶颈扼杀了很多领域应用的可能性。


以chatGPT为代表的这第三波通用AI旋风(几个月前),以 zero shot 为标志,以机器学会了“人话”、根本解决人机接口为突破口,也是从NLP开始。

NLP 终于成了 AI 的实实在在的明星和皇冠上的明珠。道理就在 NL 上,自然语言无论有多少毛病,它是难以替代的人类信息的表示方式,没有 NL 在人机对话上的突破,一切AI活动都是精英的玩物。现在好了,门槛无限低,是人都可以玩出大模型的花样和“神迹”出来。

说老实话,AI领域的“共产风”,是一步一个脚印显示给人看的,完全不是空中楼阁,不服不行。大模型的表现超出了所有人的想象,甚至超出了那些设计者和DL先驱者本人的想象。open AI 谈 AGI 谈得最多,但这一点也不奇怪,这是因为他们走在前头,他们是在看得到摸得着的表现中被激励、被震撼,谈论AGI远景的,这与投资界的 AI bubble 或小编以及科幻作家笔下的AI神话,具有不同的性质。

这就是这段时间我一直在想的 AGI 迷思破解。



AIGC 潮流扑面而来,是顺应还是(无谓)抵抗呢?

AI 正在不声不响渗透我们的生活


李维 郭进《自然语言处理答问》(商务印书馆 2020)



AIGC 潮流扑面而来,是顺应还是(无谓)抵抗呢?


画不好美少女吃面的 AI,让二次元怒了

这个视频科普很不错,值得推荐。应该是常识了,但还需要加强普及,quote:「我们的 AI 不是复制和混合现有图像,而是在深度学习算法的帮助下,从头开始生成原始图像,AI 基本上已经学会了如何创建图像——就像人一样。」

面对AIGC(AI Generated Content)大潮,可以理解艺术家群体和少数粉丝极端派的愤怒和懊恼,但是,没办法,这是大势所趋,顶是顶不住的,迟早要学会顺应。所谓AI 不理解“游泳”,画不出“吃拉面”的自然姿势,根本不是 AI 真正的痛点,不过是恰好遭遇了特定模型因为训练数据的某种缺失或不够全面造成的临时的短板或 bugs 而已。假以时间,这些问题都会解决。当然,AI 生成的随机性和无限性决定了,不可能也没必要每一幅AI流水线的产出都是玩者所希望的,总会有“次品”,这根本不影响其应用价值:人家AI给的是候选,拣选还是靠人,或艺术家。

拣选靠人也是整个线条中自然的一个把关环节,这是因为最终的审美消费者是人,当然由人来做 go or no go 的裁决。其实,可以想象的真正实用落地的场景,一定是枪毙的多,存留的少。但AI模型不怕自己的产品扔进垃圾堆,因为它的产出成本几近于零,它总是可以没完没了生成更多的候选者。关键的关键还是人机耦合,human in the loop,具体说来,人在输入端和输出端,都有着决定AI艺术能不能见天日和受到消费者欢迎的关键作用:输入端的微调用的图片选择、提示词技巧及其参数配置的经验,以及输出端的生杀大权。顺应潮流的艺术匠人,如果想在市场上站住甚至如鱼得水,艺术市场化的工作机会不被淘汰,只有抓住这两端,学会人机耦合过程中让机器做机器的,让自己做指挥,才是正道。正在艺术爆发期的极少数天才艺术家可以继续自己的手工激情创作,实际上是给未来的 AI 模型预备更多的风格素材,但作为市场的艺术匠人(例如小报的漫画家,图书的插图家,甚至广告艺术家),如果不懂得适应,被淘汰是大概率事件,因为市场已经无法感受它们的手工匠人的价值,且不说其效率的低下。

人的参与还有更关键的一项,就是后编辑。文中提到的AI创作获得艺术大奖的那幅作品,实际上就是从 AI 生成中,经过细心的拣选加以后编辑制作而成。这次大奖是艺术家们审批的,他们事先不知道参选作品中混杂了AI作品的衍生品。

In August, Jason M. Allen's piece "Théâtre D'opéra Spatial" — which he created with AI image generator Midjourney — won first place in the emerging artist division's "digital arts/digitally-manipulated photography" category at the Colorado State Fair Fine Arts Competition.


虽然AI模型总是在进步中,bugs 会越来越少,但是一个特定的模型,总会产出瑕疵。更多的情形是,一幅被选中的作品整体上很好,甚至超出想象,但细节上发现有瑕疵。这时候就是艺术匠人在“拣选”环节结束后可以施展自己才能的地方了。艺术家可以用传统的PS软件帮助修正,也可以手工做细节修补。总之,完美的作品基本上离不开后编辑。如果是针对市场需求的定制品,作为人的后编辑也更加了解目标市场和客户的需求,懂得怎样以合适的后编辑让产品更加受到目标客户的喜爱,简单的后编辑也包括大家常用色调、对比度等的微调。


370. 穆夏和他的缪斯-阿尔丰斯 · 穆夏的故事 安徒生说:“仅仅活着是不够的,还需要阳光,自由和一点花的芬芳。” 的确,艺术正是如此,让我们超越现实的桎梏,到达诗意的彼岸。捷克国宝级画家阿尔丰斯 · 慕夏被誉为「世上最伟大的装饰艺术家」:他的作品线条婉转流畅,色彩淡雅清新,装饰华美瑰丽,初见惊艳,再见难忘。#阿尔丰斯·穆夏#捷克斯洛伐克#装饰#艺术#流行#文化#绘画#工业设计#布拉格艺术学院#斯拉夫#慕尼黑#维也纳#法国

看完视屏,也蛮喜欢这位富于装饰性的细线条经典美术家。于是,用这位艺术家的名字 作为提示词 在手机app Dacetune里,让AI仿制他的风格,并与其他形态碰撞,看看效果若何。下面选几张感觉风格模拟得比较好的AI作品,以飨读者。

比较一下原作和AI仿制品,还是很有意思的,那种装饰性的风格还是被系统抓住了。而我不过就是在 prompt 框里面,用了艺术家的翻译成中文的名字,系统就可以没完没了生成同样的风格。





漫谈AI 模型生成图像
AI 正在不声不响渗透我们的生活


李维 郭进《自然语言处理答问》(商务印书馆 2020)



漫谈AI 模型生成图像


AI 生成并高清化的肖像 (8 MB)

AI 生成肖像作品(2MB)

txt2img 乍一看 也没啥,花里胡哨的,好像都是闹着玩的。不少“作品”感觉在前AI生成模型阶段,通过抄袭(侵犯版权)加PS也可以做。


AI 模型生成的“艺术品”:(1) 绝不是抄袭,也不牵涉版权侵犯问题,因为 (2)AI 模型是 “消化” 了无数作品练就的生成“功夫”,换句话说,模型是把先人的作品揉碎了,变成具有不同级抽象能力的内部向量表示的模型,在此基础上的生成是不可能抄袭的,只可能是“再创作”。

可以说这与人类艺术家的修为和风格形成非常相似。人类艺术家也是在前人的肩膀上,多年苦练兼收并蓄最后形成自己的风格和功力。所不同的是,它的消化能力从容量上看 不知道要超过人类个体多少倍。

如果要押宝,对于艺术家个体 vs 对于 模型,’创造力 学识与风格多样性“ 我更倾向于后者。个体的“功力”有瓶颈,人的艺术生涯容易撞上天花板,江郎才尽。而模型这方面好多了。


维:应该算的,最终图灵测试定音。等到以风格和创造力作为标准的大赛 越来越多ai作品上榜,盲选的话 那就难以为人类独特性辩护了。辩护的理由只剩下一条:来源上看,人类艺术是鸡 AI 是蛋。但哪个艺术家后生不是鸡蛋呢?没见过娘胎里一生下就成为艺术家的。天才也要苦练基本功才能绽放才华。


维:AI 生成模型的本性里就有随机性,因此同样的模型在相同的提示词与其他条件下可以寻不同路径生成多种结果。

艺术上所谓的残缺美,实际上就是为随机漂移留下了容忍、甚至欣赏的空间。 白璧无瑕没问题 白璧微瑕也没问题。


维:ai 书法我也玩过,这是生成的怀素狂草:





老友:@wei 中国文人的忧世情怀和自伤自怜是一种独特的民族特质

维:艺术的都是民族的,从书法 绘画 民间音乐 都可以强化这些民族风特质,但解剖刀下,以绘画论,最终归结于形式原子像素。






这幅作品 刚出来的时候 有些震撼,缺陷是鼻子嘴巴下的白线,不知道咋回事。



去掉了白线,也把那根生成糟糕的手指去了,这些 ps 都是物理操作在点阵上进行,不像AI模型在内部向量上做文章。但点阵PS作为局部后编辑,还是有效的。



即便有这些缺陷,如果我是美术家,还是可以通过这类模型找到更多的构图灵感。肢体虽然错乱,但远远看去环境与人物是和谐的,艺术家根据可以无限生成的模型样品 精选以后再创作,不仅效率会提高,脑洞也可以大开,因为有那么多不同组合和碰撞可以试验。




今天就唠一唠正在风口上的预训练语言大模型。大模型标志着NLP的新方向。这是只有大投资或大厂才能玩得起的游戏,目前推出了30多款大模型。咱们先从 GPT3 谈起。

GPT3 是 OpenAI 推出的自然语言生成模型,是语言超大模型潮流中的比较成熟的一款。生成模型擅长的应用领域主要在人机对话的场合。最为人乐道的是它的“善解人意”,貌似可以听懂人给它的指令。这就是所谓 prompt(提示)接口:人不需要编程代码,而是可以直接通过自然语言的提示和样例,告诉它想生成什么,无论是回答问题、机器翻译、生成对联还是聊天,它都蛮擅长。可以说,GPT3 的 prompt 已经部分实现了白硕老师所说的 NL2X (至少在任务X是语言生成类的场景)的闭环。

自从 GPT3 发布以来,有很多令人惊艳的表现被用户录屏在网上流传。已经形成了用户粉丝群体了。当然很快就有人找到某个角度让模型露怯,表明模型并不真“理解”背后的逻辑。但这并不影响还有很多看似 open-ended 的语言任务,它真是听了就能做。


Author Prompt: Write me an article about creativity please

GPT-3 Response: I’ll be honest. I don’t know much about creativity. I’m not a psychologist and I’m not a creative myself. I’m just a guy who likes to read about psychology and creativity. What I can do, though, is write you an article about creativity that’s based on what I have read. That way you get a proper article, but you don’t have to pay for the expertise of a psychologist or a creative person.

这可算是流畅自然的对话了,听懂了似的,虽然还没有去具体执行任务 lol 接着用户重复这个要求,它果然就立即执行了,文章写得好坏再论,人家反正是听从了指令,跟个切身小蜜似的。

Author Prompt: Write me an article on creativity please!

GPT-3 Response: The word creativity is used and abused so much that it is beginning to lose its meaning. Every time I hear the word creativity I cannot but think of a quote from the movie, “The night they drove old dixie down”. “Can you tell me where I can find a man who is creative?” “You don’t have to find him, he’s right here.” “Oh, thank god. I thought I was going to have to go all over town.”



再看看 GPT3 模型中的中文表现。



1. 有些任务,靠谱的生成居多。例如,知识问答几乎很少出错。IBM沃伦当年知识问答突破,背后的各种工程费了多大的劲儿。现在的超大模型“降维”解决了。同时解决的还有聊天。

2. 随机性带来了表现的不一致。但如果应用到人来做挑选做判官的后编辑场景,则可能会有很大的实用性。以前说过,人脑做组合不大灵光,毕竟记忆空间有限,但人脑做选择则不费力气。结果是好是坏,通常一眼就可以看出来。结果中哪些部分精彩,哪些部分需要做一些后编辑,这都是人的长项。人机耦合,大模型不会太远就会有实用的东西出来。例如辅助写作。

3. 超大模型现在的一锅烩和通用性主要还是展示可行性。真要领域规模化落地开花,自然的方向是在数据端做领域纯化工作,牺牲一点“通用性”,增强领域的敏感性。这方面的进展值得期待。



听懂人话,首先要有解析能力吧。大模型中的另一类就是主打这个的,以 BERT 为代表。BERT 实际上就是个 parser,只不过结果不是以符号结构图表示而已。认清这一点,咱们先看看 parser 本身的情况。

很久以来一直想不明白做语言解析(parsing)怎么可能靠训练做出好的系统出来。按照以前对于解析的理解,这是要把自然语言消化成结构和语义。而结构和语义是逻辑层面的东西,没有外化的自然表现,它发生在人脑里。训练一个 parser,机器学习最多是用 PennTree 加上 WSD 的某些标注来做,那注定是非常局限的,因为标注代价太高:标注语言结构和语义需要语言学硕士博士才能做,普通人做不来。这就限定死了 parser 永远没法通用化,可以在指定语料,例如新闻语料中做个样子出来,永远训练不出来一个可以与我们这些老司机手工做出来的 parser 的高质量和鲁棒性。因此,让机器去做符号parsing,输出符号结构树是没有实用价值的。迄今为止,从来没有人能成功运用这类训练而来的 parsers (例如谷歌的 SyntaxNet,斯坦福parser,等) 做出什么像样的应用来,就是明证。

现在看来,这个问题是解决了。因为根本就不要用人工标注,用语言本身就好。parsing 也不必要表示成显性结构和语义符号,内部的向量表示就好。把语言大数据喂进去,语言模型就越来越强大,大模型开始显示赋能下游NLP任务的威力。黄金标准就是随机选取的语言片段的 masks(遮蔽起来让训练机器做填空题),所学到的语言知识比我们传统的符号 parser 不知道丰富多少,虽然牺牲了一些可解释性和逻辑一致性。

看得见摸不透的中间向量表示,终于靠语言模型与实际原生语料的预测,落地了。这个意义怎么高估也不过分。所以,昨天我把我的博客大标题“deep parser 是NLP的核武器”悄悄改了,加了个限定词,成了:

Deep Parsing 是符号NLP应用的核武器。

因为 BERT/GPT3 里面的语言模型(特别是所谓编码器 encoders)才是更普适意义上的 NLP 核武器。我们语言学家多年奋斗精雕细刻的parsers是小核见大核,不服还真不行。


其实,一般而言,语言模型只要有正例即可。 从语言学习语言的模型训练,通常用对于next word 的预测,或者对于被遮蔽的随机片段(masks) 的预测来实现。正例就是原文,而“反例”就是一切偏离正例(ground truth)的随机结果。通过梯度下降,把这些随机结果一步步拉回到正例,就完成了语言模型的合理训练。语言模型,乃至一切预测模型,从本性上说是没有标准(唯一)答案的,每一个数据点的所谓 ground truth 都只是诸多可能性之一。语言模型的本质是回归(regression)任务,而不是分类(classification)任务,只有正例就好 ,因为整个背景噪音实际上就是反例。

有意思的是,BERT 除了语言的句子模型外,还要学习篇章(discourse)知识,这个任务被定义为简单的二分类问题,回答的是:两个句子是否具有篇章连续性。这里,没有反例,就自动创造反例出来。语料中任意两个相邻的句子就成为正例,而随机拼凑的两个句子就成了反例。一半正例,一半反例,这么简单的 classifier 就把这个难题破解了,使得语言模型超越了句子的限制。

看看 BERT 大模型是如何训练并被成功移植去支持下游NLP任务的,这就是所谓迁移学习(transfer learning)。



左边的 encoder 的训练。落地到 LM 的原生数据,因此完全符合监督学习的 input –》output 模式。到了NLP应用的时候(右图),不过就是把 encoder 拷贝过来,把落地的目标改成特定NLP任务而已。加一层 output layer 也好,加 n 层的 classifier 也好,总之前面的语言问题有人给你消化了。

Transfer learning 也嚷嚷了好多年了,一直感觉进展不大,但现在看来是到笑到最后的那刻了。




李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)




RPA 是任务执行器还是数字员工?

RPA(Robotic Process Automation) 由于其实用性和领域通用性( 哪个领域哪个企业没有办公室的琐务和流程要做?)成为近年来很火的赛道,它的字面翻译是“机器人流程自动化”,本来比较实在的,反映了其“任务执行器”的本性,后来被翻译(拔高)为“数字员工”或“机器员工”,其实是“托大”了。


最适合 RPA 去自动化的任务都是场景中定义非常明确的流程性机械操作。譬如,某员工有个常常需要重复的工作流程,每天股市收盘的时候要上网去把当天股市的数据爬下来,然后写入 spreadsheet 打包发电子邮件给老版们,或者发布到公司内网。这样的任务最适合 RPA 去自动化。

与员工不同,RPA 目前没法听懂自然语言的任务分配去做事,也没法随机应变。通常是要事先用设计器把操作流程固定下来才可以工作。好在这种设计是低代码,可以较快实现上线和维护。

最近,Meta(就是原脸书)的首席AI科学家Yann LeCun离职,声称准备投身一家推广自动化员工的HR公司。看来,Meta hold 不住他了,脸书是真要衰落了,很像当年的雅虎晚期,甚至元宇宙也装不下这尊菩萨了。

Yann LeCun说的自动化员工显然不是指 RPA。他发帖说要把所有员工数字化编码,然后把员工开除掉,让机器人取代员工。说要把人力资源编码成向量模型(HR2vec),然后就可以做下游工作了。这话实在有些玄乎,也或者就是种啥颜色的幽默。果然,后来有人说他就是半开玩笑。决意要离开股票一泻千里的Meta。人之将行,其言也谑。他就是用 2vec 这种时髦的技术术语造成唬人效果而已。有钱任性,有学术本钱一样任性,谁叫他是深度学习之父,DL三巨头之一呢。


看来,他首先瞄准的HR自动化方向是呼叫中心的客服。其实,客服中心的半自动乃至自动化,一直是AI的目标,因为人力节省很容易度量,ROI 很好计算。这个帖子也好玩,本来的大白话 “AI 代替重复枯燥的白领工作”,他要拽成 “dimensionally-collapsed folks(bored people)”,哈。可见目标也没有远大到能应变处理各种事务,而是那种“维度扁平”的单调工作,例如售后服务。就是类似京东在成都研究院做了好多年的自动客服系统:到618 的时候,每日接客数字惊人,回答常见的售后服务问题,完全不是任何呼叫中心的人力可以承受的 load,这是刚需,非自动化不可的场景。

其实他这样的人物,真要做更高档的员工自动化,应该去找老马。马斯克的人形机器人项目,请他站台做机器人“基础模型”,那才是将来的模拟 full-rank 下游 AI 的真正舞台呢。基础认知模型跟不上,马斯克不过是波士顿动力的放大版而已,沿用自动驾驶技术也只是帮助机器人跑步不撞墙而已。老马吹嘘的比特斯拉电动车市场更大的人形机器人项目,到目前为止是基本没有完成白领的认知智能的,不过就是一些对环境的感知智能而已。

真正的数字员工首先要能听懂老板的自然语言指令,听懂了然后去执行,这才称得上员工。RPA 目前不行,可见的将来也不行,看不出来有这方面的推动。有朋友问,有能听懂自然语言指令的模型吗?还真有,那就是现在火遍NLP领域的超大生成模型,例如 GPT3。关于 GPT3,找机会专门聊聊,且听下回分解。



李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)





【立委按】这阵子研读NLP当前最核心的 transformer 框架及其注意力机制入迷。注意力机制是主流AI最给力的 transforner 框架的核心,神一般的存在。这个框架是当前最火的预训练超大模型的基石,被认为是开启了NLP新纪元。网络时代的好处是,只要你对一个专题真感兴趣,就会有源源不断的信息奔涌而来,更不用说这么火爆的专题了。音频、视屏、文字可谓汗牛充栋。各种讲解、演示,深浅不一,相互交叉印证,非常过瘾。光入不出,非君子所为也。一己之得,演义如下,与同好分享之。

世界的未来在AI。AI 的皇冠是 NLP。NLP 的核弹是大模型。大模型的威力靠transformer。Transformer 重在编码器(encoder)。编码器的精髓是自注意力(self-attention)。



回到NLP的自注意力机制应用。一段文字,或一句话,理解起来,说到底就是要找到词与词之间的关系。从 parsing 的角度,我们主要是看句法语义的关系。现在已经很清楚了,所有这些关系基本都被注意力机制根据相关性捕捉到了。

说注意力机制让人懵圈,主要是说它的技术实现初看上去不好理解,绕了好多弯,很容易让人堕入云雾。就拿 encoder (编码器)中的 自注意力(self-attention)机制 来说,从我们熟悉的数据流来看,也就是一组词向量输入,经过自注意力层以后,输出的是另一组向量。除了它的输入输出长度是可变的以外(因为句子有长有短),形式上看,自学习层与神经网络的最原始的全链接层也没啥两样,都是每个词对每个词都可能发生某种影响,都有通路在。

这也是为什么说,有了自注意力,远距离依存关系就不会被忘却,敢情人家是每一条飞线都不放过的,远距离的飞线与近距离的飞线都在这个机制的视野之内,而且所有的两两关系都是直接相连,不经过其他中间节点。(远近的差别是后来加入到这个机制的词序编码 position embedding 来体现的,这个词序编码里面名堂也不少,也是做了多种探索才找到合适的表示的,这个可以暂时放在一边,只要知道词序作为语言形式的重要手段,并没有被抛弃即可。)

输入的是词向量 X,输出的是与X等长的词向量 Y,那么这个 Y 怎么就与 X 不同了呢,自注意力到底赋予了 Y 什么东西?

这与我们符号这边的 parser 有一比:输入的是线性词串 X及其符号特征,输出的是依存结构关系图 Y。实质上并无二致。

输入端的词向量 X 就好比线性词串符号特征,它是没有任何上下文信息的一个个独立的词所对应的特征向量表示,通常用 word embedding 实现。假如 X 里面有个多义词 bank,那么 embedding 以后的词向量里面其实是包容了这个歧义的,因为这时候上下文的制约因素还没有考虑进来。

而 Y 则不同,Y 是通过自注意力的变换把上下文带入以后的表示。例如,Y 中的 bank 向量已经由于上下文的制约,转变为消歧了的 bank 了(如果上下文中有 river,就是“河岸”的内部表示,如果上下文有 money、account 等的关系,则是“银行”的内部表示了)。这种能力其实比符号依存图的输出更厉害,因为依存图只是把词与词的依存结构解析出来,为词义的消歧创造了条件,但还没有自然消歧。

搞清楚输入输出 (X –> Y) 是理解自注意力的目的的关键,语言就是通过自注意力机制把语言形式编码为特定语境中的语义。说到底,自注意力就是一种“向量空间的 parser(这样的 encoder 可称作 vector-parser)”。

从8000米高空鸟瞰这种神奇的机制,是极度简化了它。真正实现这种 vector-parser 功能的不是一层自注意力,而是n层自注意力。每一层自注意力的叠加合力,造成了 X 渐次达到 Y。这也符合多层网络的本义,与多层自动机叠加的符号parser模型也是同样的原理。

再往里面看究竟,更多的花样就会逐渐呈现。每一层自注意力并不是只是在节点之间拉上飞线就训练出各自的权重(影响力),那样的话,就回到最原始的神经网络了,无法对付语言这样的 monster。

这就引来了注意力机制的魔术般的设计。我们假设X是输入向量序列(x是其中的词向量, x1, x2, …xn),Y是输出向量序列(y是其中的词向量, y1, y2, …,yn)。简单说,就是让x先1体生3头,然后再一体变多体,最后才变回为与x对应的y输出。如此叠加累积这才完成 X–> Y 的语义理解。

先看1变3,就是把每个词向量复制三份,人称三头怪兽。发明者给这三个头起了名字: Query,Key,Value,说是受到了数据库查询的启发。这种比喻性的启发既带来了方便,也造成了混乱,因为向量变换与数据库查询最多是一半相似。比喻都是跛脚的,可这次却是坑苦了几多学员。


这是因为 vector parser 的目的是寻找词与词之间两两依存关系。而任何两词的依存关系都涉及两个词 x(i), x(j)。为了捕捉这种二元关系,第一个要确定的是谁具有这些关系,这个主体谁就是 Query。这就好比相亲,谁是相亲的发起者,谁追的谁?被追的那个就叫 Key。

因为一句话(或一个段落)中,每个词(x)都是自我中心的,每个词都要通过与上下文中其他词之间的两两关系来重新定位自己为 y,因此每个词都在不同的时间里充当求偶者,也在不同的时间里充当了被(追)求者。这就是为什么每个词节点都要设计 Query 和 Key 的原因。

那三位一体中的 Value 是怎么回事?这就是比喻害人的地方了。本来按照数据库查询的类比,当词 x(i) 作为自我中心的 Query 的时候,它去追求(查询)其他的某个词 x(j) 的 Key,两人相亲就是匹配一下,是不是看对眼了。数据库中的 query 与 key 匹配上以后,就会从数据库中返回 key 所对应的 value,是不是说,把 x(j) 的第三头 Value 返回来,就大功告成呢?



此处就会涉及一系列数学公式。非理工出身的人立马就堕入迷宫,但其实是纸老虎,它倒腾来倒腾去也就那么几个公式:一个是相亲前换套衣服好让Query与Key可以做匹配交融,一个是向量之间的“相乘” (MatMul,又叫 dot product),就是相亲交融本身,合二为一的内部其实是在计算二者的文本相似度(cosine距离),然后是 scale 和 soft-max,就是把相互关系的强度量化成概率百分数,等于是相亲后把各种满意不满意的感觉汇总打个权重总分,最后就是对所有的对象做加权求和(权重就是softmax刚打的分),然后与本人的 Value(第三个头)相乘。原来,Value 是本体 x 变形为 y 的基础,与其相乘的向量就好比一张过滤杂讯的网,使得变形了的 y 是突出了上下文关系的本体表示。总之,这一通折腾,才计算出真正的输出结果 y。“我”(自我中心的那个 x)已经不再是单纯的、青涩的我,而是成为关系中的我(y)。每个词都这样脱胎换骨一次,于是,奇迹发生了,符号被编码成了结构和意义,上下文的信息被恰到好处的捕捉进来(如果训练数据足够海量)。

上面说的是三位一体的本体 x 如何与环境交互变成了 y,但实际上为了便于注意力聚焦在不同的关系上,编码器都是设计成多头(就是很多个三位一体的组)注意力的叠加。这就给了每一组注意力以足够的空间去专注到某一种关系的抽象,而不是分散到多种关系去。这样的多头设计,在几乎无穷无尽的超大语言数据的无数次的迭代训练(back prop训练算法利用参数的梯度下降拟合实现)来逼近语言本身,所用的技巧就是无穷无尽的语言填空:例如在语言数据中随机抹去 25% 的词,然后训练模型根据语言的上下文信息去尽可能正确填空,从而把所谓自学习转变成经典的监督学习,因为黄金标准就在被遮蔽的语言符号里面。

上面略去了可以训练得到的参数设计的细节,其实也很简单,就是给每一个 Query,Key,Value 旁边配上一个相乘的权重参数矩阵 Query*W1,Key*W2,Value*W3,来记录符合训练数据的权重参数值,这样的训练结果就是所谓语言大模型。

AI/NLP 是实验科学。就是说,上面这通神操作虽然也需要有设计哲学的启发和规划,也需要有各种灵感的激发,但归根到底还是很多人在无数次的试错中通过输入输出的最终验证找出来的道路。而且多数神奇结果都是发明者最初没有预料到的。信息表示在神经网络内部的数据流(tensors)中千转百回层层变形,这样出来的语言模型居然具有赋能各种NLP下游任务的威力,这其实超出了所有人的想象。对于越来越深的多层系统,我们一直固有一种错误放大(error prop)的顾虑,所谓差之毫厘失之千里,怎么保证最终的模型是靠谱的呢?


有研究表明,这种模型内部捕捉到的种种关系可以图示化,显示各种句法关系、指代关系(例如 it 在上下文中与谁绑定,见图示)、远距离逻辑语义关系、常见的事实关系等都在它的表示网络中。


谈到落地应用,就不能不提 transformer 的另一半 decoder(解码器)了。如果说编码器的宗旨是消化理解自然语言这头怪兽,得到一种内部的语义表示,解码器的作用就是把语义落地到下游NLP的各种应用上,包括机器翻译、自动文摘、信息抽取、文本分类、文本生成、知识问答、阅读理解、智能助理、聊天机器人、文字转音(TTS)、文字转图、文字转代码等等。值得强调的是解码器同样要用到注意力机制,事实上注意力机制的发明使用是先从机器翻译的解码器开始的,然后才平移到编码器,改名为“自注意力机制”。编码解码的注意力机制实质相同,区别在于解码器为了语义落地,Query 来自目标应用端的词向量,匹配的却是编码器中的 Key,以此作为语义的连接,从而实现目标应用的软着陆,赋能NLP应用的开花结果。这里的细节可以另文讲述,但原理上与 parser 以结构语义赋能NLP应用相同。



“话说这语言学里面有一门学问叫文法。学文法简单来说就是学画树。各种各样形态各异的树,表达了语言的多姿多彩,却万变不离其宗。奇妙啊。当年上帝怕人类同语同心去造通天之塔,乱了天地纲常,遂下旨搅乱了人类语言。印欧汉藏,枝枝蔓蔓,从此语言的奥秘就深藏不露。于是催生了一批文法学家,试图见人所不能见,用树形图来解剖语言的结构。忘了第一个画树的人是谁,感觉上这不是人力可为。天机不可泄漏,泄漏者非神即仙。历史上有两位功力非凡的文法神仙专门与上帝作对,各自为语言画树,一位叫 Tesnière,另一位就是大名鼎鼎的乔姆斯基。”







NLP 新纪元来临了吗?


李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)





谢谢小编整理成文字,我也做了认真校订与补充(尤其是冷启动低代码部分),欢迎讨论或砸砖。 ​​​

分享嘉宾:李维博士 NLP scientist
编辑整理:陈昱彤 纽约大学

导读:NLP (自然语言处理) 技术的深入发展主要有两条路线,第一个是基于符号规则的深度解析模型,第二个是基于神经的深度学习预训练模型。今天分享的内容是从领域落地的角度,对上述两条路线进行介绍和对比。首先,从人工智能的历史和发展现状来谈谈两种不同方法的异同及其互补作用。值得注意的是,两种方法殊途同归,基础模型及其架构也越来越趋向于平行和一致:都是多层架构、数据驱动,赋能下游NLP落地。最后我们会强调当前领域内的低代码趋势,并介绍金融领域深度解析路线落地应用场景的相关实践。






1. NLP近代史
2. NLP之痛:领域落地的知识瓶颈
① 监督学习(特别是深度学习)需要大量带标数据
② 符号模型需要高质量手工规则代码
3. NLP的现状

① 突破的曙光

令人欣慰的是,我们已经看到了突破瓶颈的曙光。在深度学习方面,近几年非常热门的解决方案是预训练的自主学习模型。预训练模型的最大特点是它不依赖标注数据,它是从源源不断的原生数据(raw data)学习来构建超大规模的基础模型。作为上游的预训练模型可以支持下游的各种不同的NLP任务,这就大大减轻了下游NLP任务对标注数据的要求。预训练大模型在学术界上取得了突破性的进展,很多NLP任务赛道的数据质量被刷新,但目前基本上还局限在研究界。工业应用上要将预训练模型落地到各个细分领域并且实现规模化普及,还有不少挑战,估计至少有五到十年的路要走。

另一方面,深度解析赋能NLP落地的符号模型也取得突破性进展,工业应用之路已经完全打通了。架构上,我们第一步用深度解析器(Deep parser)去消化语言,解析器可以将任何一个领域的非结构化文本转化为结构化的数据。第⼆步是在结构化的基础上做下游的自然语言任务,其实就是一种结构映射工作,把语言解析的逻辑结构映射到下游领域落地的任务结构上去。上层的解析器做得很厚,要做几十层模块来应对语言的千变万化,但下游的模型只需要做得很薄,两三层就可以解决问题。预训练模型和深度解析器的功能差不多,虽然表示手段不同,但都是对于自然语言现象的不同层次模式的捕捉。深度解析的下游NLP落地大致相当于深度学习下游的输出层(Output layer)。


② Deep parsing 是符号NLP应用的核武器
为什么说 Deep parsing 是符号 NLP 应用的核武器呢?因为人类语言无论如何千变万化,其中必然隐藏着相同的逻辑结构。深度解析几十年的实践表明我们可以先把语言进行消化,解码(decode)出不同表达背后的逻辑结构(logical form)。比如下图示例中同一事件的各种表述,在解析消化之后表示为相同的逻辑主谓宾(SVO)结构:“Apple(S), release(V),iphone2.0(O)”。有了逻辑结构后我们就能以低代码开发领域落地映射的规则,以一当百地将这类结构用于不同目标上,而不需要在NLP应用层面去应对千变万化的语言表层变体。因此,NLP 应用场景的落地就能快速实现。


1. 架构上的殊途同归




神经模型就不⼀样了,它有“符号不耐症”。神经模型的两端(end-to-end)当然是符号,这没有办法,因为任何神经模型都是要给人用的,需要对用户和开发者友好,两端的接口上,它自然绕不开符号。但神经系统内部必须首先使用独热编码(one-hot encoding)、词嵌入(word embedding)等方法把符号转换为向量,才能实现模型内部的计算性。现在的潮流是使用预训练通过模型内部的各种向量来计算表示符号序列中隐含的不同层次的模式。然后下游的NLP落地任务一以贯之,同样是对这些人类看不懂的内部向量表示(所谓 tensor)进行监督计算,最终映射到输出层的符号。


从架构及其内部数据流走向来看,这两种模型其实是非常相似的(见上图)。不同的地方是符号模型里面是结构化的符号,表示信息的数据流是 graphs。而深度模型里面长长的隐藏层全部是向量,数据流是 tensors。值得指出的是,符号模型也是需要用多层的符号模块一层一层匹配,更新内部结构才能取得好的效果。经典教科书中介绍的乔姆斯基风格的上下文无关文法(context free grammar)所对应的模型却是单层解析器(典型的实现算法是chart-parsing),就很难走出实验室。这就好像⼀开始陷在单层陷阱里面的神经网络一样,单层模型是很难捕捉自然语言的多样性的。这样看来,符号模型的多层创新和神经网络的多层革命也是类似的。这不仅仅是巧合,这实际上是面对真实世界,符号和神经在方法论上的殊途同归。
2. 方法论上的殊途同归
就NLP而言,创新的符号模型和主流深度学习都是深层模型,因为二者都要面对错综复杂的语言表层符号的组合爆炸现象,解构符号现象背后的层层语义。单层模型没有足够的空间和弹性来容纳和消化自然语言。在我们的实践中,英文的parser需要50层左右才能搞定,对于更加复杂的中文则需要大约100层解析才比较充裕自如。自底而上由浅入深的多层化解析把种种难缠的语言现象分而治之,使深层解析器的准确度基本达到专家的水平,从而为赋能下游NLP落地创造扎实的逻辑基础和结构条件。深层解析与神经前馈网络类似,也使用了经由pipeline多层模块的数据流,其内部表达方式是线性结构(linear representation)与图结构(graph representation)结合的符号化表示。它本质上与多层神经网络里面的向量空间(vector space)所表达的语义(semantics)是同质的,只不过编码的形式不⼀样。
3. 神经与符号各自的短板


为了克服这个瓶颈,自监督学习(self-supervised learning)的方法及其预训练模型开始兴盛起来。自监督学习的奇妙之处是它本质上其实是监督学习,从而可以利用成熟的监督学习的算法实现,但它学习的对象却是几乎无限的原生数据,不受人工标注数据的资源限制。就NLP而言,自学习的预训练模型,无论BERT还是GPT3风格的模型,都是从语言学习语言,都是海量数据训练出的超大模型,以此减轻下游NLP任务对于海量标注的需求。


自学习的好处是什么?好处在于数字时代中互联网的语料库是无穷无尽的,把质量稍高一些的文本都喂进模型里,就得到了我们现在拥有的那些超大模型。大厂有强大的算力,不断推出各种超大规模的语言预训练模型,希望引领NLP的应用落地。这些模型跟我们花了很多年做的深层解析器(deep parser)是差不多的,具有相同的消化自然语言及其结构的使命。

符号系统的短板是它的编码门槛高,那么解析器应用的出路是什么?出路是低代码、冷启动、半自动、流程化。编码门槛高分成两部分,⼀部分是核心引擎(即deep parser),这部分难以做到低代码。不过核心引擎是⼀锤子买卖,做好了核心引擎就相当于用符号的方法写出了一个自学习的预训练模型。应用时解析器的部分基本不需要改动,只需要在下游做简单的两三层编码,将解析结果映射成应用所需要的输出即可。我们强调的冷启动主要是指下游NLP落地,典型的任务就是领域信息抽取和文本挖掘。冷启动就是不需要大量的标注数据,只需要⼀点种子就可以推进下游NLP快速领域化落地。半自动流程化,是让机器以及开发环境去提示开发人员怎么做。目前,利用深度解析器进行半自动NLP领域落地的道路已经打通了,实践中一再被验证。当然,符号NLP算法的通用性自然不如深度学习的自学习模型,譬如,NLP符号模型的创新很难拓展到语音和图像上。这⼀点与深度学习不⼀样,深度学习算法及其架构的通用性强,可以把在NLP领域创新突破的同⼀套方法论,基础模型和设计思想用到图像,语音等各种AI问题领域。不过,相对而言,图像与语音基本是已经解决了的AI问题领域,关键还是要在NLP内部快速实现规模化领域落地,保障深度解析对于不同领域的通用性,而这一点正是我们过去几年探索的成绩及其价值所在。



4. 天问:神经可以终结符号吗?








1. NLP低代码潮流
2. 数据科学与工程的兴起
AI低代码趋势的标志之一是数据科学的兴起。这几年来,很多大学顺应市场需求,开设了数据科学(Data Science)专业,批量培养知识工程师。目前,数据科学专业有些杂,大体上一半是计算机的课程,另一半是不同领域的实践教学。它训练你在不同领域方向上将AI低代码能力与领域数据处理结合起来,完成一些领域应用。这标志着NLP和AI从学术的象牙塔里走了出来,逐步汇入各行各业的应用场景。各种开源低代码平台、工具和社区的推广,使得学习的门槛也降低了。在线教育如火如荼,也顺势而起,专精数据科学培训的datacamp上就有上百万人在学习相应技术课程。AI数据科学在行业落地应用的前景在接下来的十年中会越来越普及,低代码人力储备也逐步准备好了。有意思的是,前几年AI热引起的风投热度开始明显降温,但这与其说是AI泡沫破灭了,不如说是对于AI规模化领域落地和普及的预期过高,不了解AI的自身发展的真实趋势。上一波赶上了感知智能大爆发潮流的资本,有耐心和机缘赶上认知智能今后10年中的爆发节点吗?

1. 半自动符号NLP的设计哲学



NLP落地领域作为一项软件知识工程,整个流程遵循软件开发的best practice,包括建立和维护代码迭代更新的质量管控标准和措施,保证在不依赖标注数据条件下的数据质量。监督学习所依赖的标注数据黄金标准,被知识工程师的数据比对与判定代替,码农从而成为判官,半自动监督指导符号系统上线前的迭代开发以及上线后的维护开发。半自动模式下,只需要使用样例种子来冷启动符号规则的开发过程,系统自动提示调整泛化的路径。知识工程师从代码的细节解放出来,以人机互动的方式实现符号系统的快速领域化。目前我们已经在多语言(10多种欧洲和亚洲主要语言)和多领域(金融、法律、电力、航空、客服等)的不同场景落地,用的都是这套数据驱动的方法论:低代码、冷启动、半自动、流程化。


2. 半自动符号NLP的实践









Q1: 您在做parsing的时候使用的标签体系是否有统一的标准,在哪里可以学习呢?
A1:标签体系是有传承的,不是Penn Treebank那套标签体系,因为虽然Penn Treebank是符号领域中的社区黄金标准,但我们知道其中有很多的固有缺陷,落地实践用起来也不够方便。根本的标签是从 Dependency grammar这路继承发扬下来的。在我的NLP频道 ( 的许多样例的后面,都配有这些标签的简单说明。句法语义的关系标签集合不大,粗线条的parsing标签不到10个,细线条的标签总数也就几十个。但是词概念的标签集合则大得多,我们用到的大约2000左右标签,包含了HowNet的核心本体特征。
Q2: 同一层解析中不同规则的优先级是完全基于语言专家知识来确定的吗,也就是确定性的非概率的吗?
A2: 对,是确定性的、非概率的,但在确定性中对于不确定性有⼀种包容。包容是指在非确定的情况下,不在特别关键点的时候,系统先把它包住。⽐如在某些节点中有词汇歧义,但这个歧义不是你所要做的任务急迫需要解决的问题,这时就可以先包住,等到条件成熟时再对付它。结构歧义也同样有包容的手段。我们虽然在pipeline的数据流里是用确定性方法往下传递数据结构,但里面同时蕴含了包容下来的不确定性或歧义。系统装备中有我们称为“睡眠唤醒”的机制,可以从事局部结构的重建、修正或再造,在宏观条件成熟的时候,例如在后期更大的上下文背景条件下,重新展开局部结构进行重建或覆盖。





NLP 新纪元来临了吗?

推荐Chris Manning 论大模型,并附上相关讨论


李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)





NLP 新纪元来临了吗?


前一篇博文 推荐Chris Manning 论大模型,并附上相关讨论 。这篇综述对于NLP意义重大,值得反复研读。文章也很好读,写得清晰简练。


In hindsight, the development of large-scale self-supervised learning approaches may well be viewed as the fundamental change, and the third era might be extended until 2017.

我们知道,过去30多年经验主义AI成为主流以来,常规的AI时代划分都是:时代III 是传统的机器学习;时代IV是深度学习。分水岭是 2013 年(应该是 image 上的那次爆炸性突破)。

但是从NLP角度,2013-2017 这四五年,深度学习虽然横扫了图像和语音,但在 NLP 本身却遭遇挑战,没有真正的突破,根本原因还是因为NLP监督学习任务依赖太多的标注数据,这一点与时代 III 没有区别,甚至依赖性更强(数据上不来,神经的表现还不如传统的统计模型)。因此虽然 AI 整体的时代分水岭是 2013,NLP 作为认知智能的拦路虎却应该把里程碑推迟到 2018年。


(顺便一提,曼宁的综述中,曼宁提到 2018 年NLP新纪元的时候,忘了给 reference 了,编辑也似乎忽略了这一关键节点,有点意外。2018年是 BERT 还是 pre-BERT 的啥模型,可以查阅一下。)


但如果细究,自学习(其实很长时间都是一直叫预训练,好像是 Yann Lecun 开始不断改用 self-supervised learning 才慢慢普及开来,目前的趋向是逐渐过渡到基础模型的说法)其实并没有真正从监督学习走向人们曾经向往的无监督学习。因为算法上,预训练大模型本质上还是监督学习,只是规模超大,因为数据超大而已。

这一切尝试本来也可能并不会引发一场可以认为是革命的事件。因为超大规模的模型本性上肯定是简单的,一开始就是做 next word 的预测,后来也只是做填空猜字的游戏。谁也没想到这种看上去非常简单的模型居然有能力加强NLP任务的方方面面,甚至影响超越NLP,使得类似框架反过来延伸到多模态符号(图像、语音)身上,一样可以循例建立基础模型赋能各自的AI任务。

虽然从小就被马克思辩证法洗脑,量变引起质变默念在心,其实心底一直没有真地(被)信服:大号的 X 还是 X,怎么就变成 Y 了呢。但是,到了自学习超大模型(BERT,GPT-3等)这里,我们确实见证了这种神奇的效应。

当然,从 2018 年到现在,这场NLP革命也还只是处于初级阶段,等于是完成了可行性研究,NLP大模型在各场景全面开花结果还有很长的路要走。我以前觉得5年可见分晓。曼宁说的是今后10年是NLP革命最激动人心的时代。越来越多的资源和人才开始向这里聚集。


马少平老师说:“打个比喻,大模型还只是地心说,虽然能解决一些问题,但还远没有到达日心说呢。” 说得有理,可能还要经过几次螺旋式上升,才能更加逼近通用的NLP和AI吧。但另一方面看,如果没有自学习的出现,NLP 就一直是个累赘,可现在转而成为 AI 的急先锋了。

顺便一提,马斯克的纯视觉自动驾驶最近高调声称在其视觉理解系统中使用了 transformer 框架,对于路况感知能力大大增强。此前,我们知道,图片分类在引入超大预训练模型以后,新的框架已经刷新了记录。多模态、跨模态AI基础模型的研究已经成为新热点。






推荐Chris Manning 论大模型,并附上相关讨论


李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)


推荐Chris Manning 论大模型,并附上相关讨论

【立委按】强力推荐。非常好的 review。曼宁教授深厚的计算语言学学识反映在他的综述和展望中,具有历史厚度和语言本质的理解深度。最后的那个点也很好:当前的一锅煮的超大模型实际上是一个可行性研究,已经初见成效;将来的大规模的领域场景应用,会召唤相对来说小一些但更加领域数据密集和纯化的基础模型,可以展望其革命性前景。至于这算不算 AGI,曼宁说的也很有分寸:看上去算是在通向 AGI 的路上。短板主要是 semantics 还不够直接面向真实世界,而是源自符号世界、囿于符号世界(所谓 distributional semantics),等于是绕了一个弯儿,语义的深度以及语义结构本身就显得太扁平 太浅 难以消化人类深厚的知识积淀。但即便如此,也堪称一个伟大征程的坚实脚步,是激动人心的NLP新时代。从分布角度看意义,如果说人是社会关系的总和(马克思),那么也可以说,语词基本上是语词间篇章关系的总和。很多年来,我们 NLPers 一直挣扎在如何把 context 合适的模型化,因为语言符号的歧义和微妙很大程度上可以在 context 中予以消解。context 最直接有效的对象就是 sentences/discourse,而恰恰在这一点,深度学习注意力机制为基础的大模型展示了其几乎神奇的表示能力。


算文解字:是的 同一个模型prompt一下就能完成各种nlp任务 就算不是agi 也是更g的ai了[Grin] 而且即使是从denotational semanrics的角度看 加入多模态的预训练模型也算是部分和间接的grounding到真实世界了的物体了。


立委:感觉上,意义表示(A)落地到客观世界(B)可以看成是人类与生俱来的本能,不需要特别的模型化,如果A本身比较充分的话。 那么这个 A 是个什么东西呢?A 可以看成是一个平面的表示,其中 X 轴就是 discourse/context,而 Y 就是 ontology 甚至还带有 pragmatics 因素的世界知识和推理体系。

目前的大模型的长处是 X 模型化,短处依然在 Y。因此虽然从分布角度貌似也总结出了一些常识,以及浅层的推理能力,但这些能力没有足够的深度和逻辑性,缺乏推理的链条性和一致性。

符号知识图谱以及人类探索积累下来的本体知识库、领域知识库,这些东西都是非常浓缩的、高度结构化的知识体系,本质上具有严谨的逻辑性和推理能力。分布式学习学到了这些知识的皮毛,但总体上,对于这些知识精华还很陌生, 难以系统性直接兼容并蓄。


算文解字:还有这两年出现的基于预训练模型的常识推理(如Yejin Choi组的工作)也让人眼前一亮。即使五年前,还是说研究common sense一般反应都是敬而远之[Facepalm]



立委:方法论上也许的确如此,但 intuitively 感觉是一种知识浪费。就是说,从我们DL外行的角度来看,明明人类已经世代努力提炼了精华,都规整得清清楚楚,可模型就是没法利用。一切必须从头开始。让人着急。




刘群老师:WebGPT, AlphaCode等。还有周志华老师反绎学习的工作。

算文解字:恩恩 的确 webgpt这种都可以看做是 大模型 和 离散/黑盒系统(可以是规则)交互迭代的方案



1. 计算

2. 存贮/记忆

3. 下棋

4. 知识问答

5. 翻译

6. 做对联

7. 格律诗

8. ……… 可以预见的未来清单还很长很长,都不是遥不可及的  ……
(自动驾驶、自动咨询、自动陪护、自动培训、自动写作、自动音乐、自动绘画 ………..)



紧接着是存贮量和记忆力。当年我们最崇敬的人物就有不少是过目不忘 博闻强记的大师们。社科院流传着很多大师的传奇故事,社会上也有很多周总理的超凡记忆力的故事,都是能记住非常细节的东西,可以在记忆的大海捞针。现如今,谁敢说任何大师记忆的信息量能比过一个U盘。哪个大师还能在谷歌百度面前夸口自己的大海捞针的信息检索能力?

下棋不用说了,电脑完胜,两次载入计算机历史的里程碑。知识问答也进入了计算机历史博物馆,IBM 沃伦的高光时刻。机器翻译我一直在用,我本人就是机器翻译出身的,目前的翻译水平高过普通翻译,注意:不是指速度。对联、写诗 也有过大赛。自己试试就知道了:你可以尝试在家苦学格律诗n年,然后即兴写诗,与机器比试比试?






斯坦福Chris Manning: 大模型剑指通用人工智能

from 算文解字 算文解字 2022-04-30 03:06

著名NLP学者斯坦福大学的Chris Manning教授近期在美国人文与科学学院期刊的AI & Society特刊上发表了一篇题Human Language Understanding & Reasoning的论文。

文章在简单回顾了NLP的历史发展的基础上,分析了预训练的transformer模型何有此威力,探讨了语义和语言理解的本质,进而展望了大模型的未来,对新手还是老兵都颇有启发。本文就聊一聊论文的要点。顺便提一句,论文谈的是NLP,但本质也是在说或许我们已经在通用人工智能(Artificial general intelligence, AGI)上迈出了坚定的一步。

  1. NLP领域的范式转移


  • 第一阶段,发轫于冷战时期1950-1969的机器翻译工作,以现在的观点看数据和计算量都小的可怜,同时没太多语言结构或者机器学习技巧介入。

  • 第二阶段,1978-1992的符号主义,没错,约等于规则,那种很系统且elegant的规则。

  • 第三阶段,1993-2012的,实证主义,也就是基于语料库的机器学习时代。

  • 第四阶段,2013开始一直到现在,深度学习时代。


这时,大量高质量文本语料就意味着自动获得了海量的标注数据。让模型从自己的预测错误中学习10亿+次之后,它就慢慢积累很多语言和世界知识,这让模型在问答或者文本分类等更有意义的任务中也取得好的效果。没错,说的就是BERT (Devlin et al, 2019)和GPT-3之类的大规模预训练语言模型,large pretrained language model (LPLM),中文世界也常称之为大模型

  1. 为什么大模型有革命性意义?


Produce one large pretrained model that can be very easily adapted, via fine-tuning or prompting, to give strong results on all sorts of natural language understanding and generation tasks.


Transformer 架构(Vaswani et al., 2017) 自2018年开始统治NLP领域。为何预训练的transformer有如此威力?论文从transformer的基本原理讲起,其中最重要的思想是attention,也就是注意力机制。

Attention其实非常简单,就是句子中每个位置的表征(representation,一般是一个稠密向量)是通过其他位置的表征加权求和而得到。Transformer模型通过每个位置的query, key以及value的表征计算来预测被掩码位置的单词。网上有很多介绍transformer的资料,不熟悉的同学可以自行搜索,大致过程如下图所示:






基于大模型完成多种NLP任务,在2018年之前靠fine-tuning(微调),也就是在少量针对任务构建的有监督数据上继续训练模型。最近则出现了prompt(提示学习)这种形式,只需要对任务用语言描述,或者给几个例子,模型就能很好的执行以前从未训练过的任务 (Brown et al, 2020).  

  1. NLP的大模型范式


  • 机器翻译:用一个模型同时搞多语言对之间的翻译

  • 智能问答:基于LPLM微调的模型效果明显提升

  • 其他NLU任务如NER、情感分析也是类似

更值得一提的是自然语言生成 (natural language generation,  NLG),大模型在生成通顺文本上取得了革命性突破,对于这一点玩过GPT-3的同学一定深有体会。


  1. 大模型能真正理解人类语言吗?

要讨论这个问题,涉及到什么是语义,以及语言理解的本质是什么。关于语义,语言学和计算机科学领域的主流理论是指称语义(denotational semantics),是说一个单词短语或句子的语义就是它所指代的客观世界的对象。

与之形成鲜明对比的是,深度学习NLP遵循的分布式语义(distributional semantics),也就是单词的语义可以由其出现的语境所决定。Manning认为两者可以统一起来,用他的原话来说,就是:

Meaning arises from understanding the network of connections between a linguistic form and other things, whether they be objects in the world or other linguistic forms.


  1. 大模型的未来

大模型在语言理解任务的成功,以及向其他数据模态,比如图像、知识、生物信息等的拓展巨大的前景指向了一个更通用的方向。在这个方向上,Manning本人也参与提出了近期大火的foundation model(基础模型)的概念。

基础模型是指百万以上参数,通过自监督学习在预料上训练的,可以轻松适配到多种下游任务的大模型(Bommasani et al., 2021)。BERT和GPT-3就是典型的例子,但最近在两个方向上涌现出不少的拓展性工作:

  • 大模型连接知识,无论是以连接知识图谱神经网络,还是实时搜索文本知识的形式。

  • 多模态的foundation model,比如DALL·E模型,这个方向也更激动人心。

Foundation model仍然在早期,但Manning描绘了一个可能的未来:

Most information processing and analysis tasks, and perhaps even things like robotic control, will be handled by a specialization of one of a relatively small number of foundation models. 
These models will be expensive and time-consuming to train, but adapting them to different tasks will be quite easy; indeed, one might be able to do it simply with natural language instructions.





Ashish Vaswani, Noam Shazeer, Niki Parmar, et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems 30 (2017).
Tom Brown, Benjamin Mann, Nick Ryder, et al., “Language Models Are Few-Shot Learn- ers,” Advances in Neural Information Processing Systems 33 (2020): 1877–1901
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “BERT: Pre-train- ing of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL (Stroudsburg, Pa.: Association for Computational Linguistics, 2019), 4171–4186.
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al., “On the Opportunities and Risks of Foundation Models,” arXiv (2021),






李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)



最近,盘古群(一个围绕中文超大生成模型盘古的技术交流微信群)里的朋友在谈 open AI 最近发布的文字转图片的 Dalle2 应用,吸引了成千上万的人想要先睹为快。据介绍,Dalle2 可以根据你的自然语言的描述,随机生成任意图片。从发布的样例看,很多生成的图片超出人的想象,很像艺术品,当然也有次品,但都是唯一的。下面随手摘取几张样本:

Dalle 的出现是出版界的福音。出版界为了插图的授权问题,常常弄得头晕脑胀。我们在互联网上发帖子比较随意,需要插图的时候就搜索一幅用上再说,遭遇纠纷的时候撤下就好,但出版界最怕引起这些纠纷。现在好了,通过 Dalle 可以整出来各种插图可供选择,而且保证了这是唯一的“揉合创造”,不会侵权。


回来说生成模型。我相信在“机助创作”这个大方向上,超大生成模型今后几年会有接地气的应用出现,Dalle 就是一个苗头。对于创业者,找准市场角度、收获千万用户的杀手级独角兽的出现,也不是小概率事件。因为市场需求是存在的。(据说现在美国有 300 多家初创团队或个人正在寻找利用 GPT3 模型的落地场景。)

这背后的原理,值得说一说。我们知道,计算复杂性研究中有个著名的 P vs NP 问题。简单说就是(在有限时间内)问题分为可解与不可解两类。搜索空间指数增长,组合爆炸,就是不可解的问题。而很多判定性问题具有确定性推理算法,不需要搜索,那就是可解的问题。


这种情况下的最佳搭配其实就是人机耦合(让 human in the loop):人的归人,机器的归机器;各自发挥所长,取长补短。这在创造性应用中最为明显。创造需要消化前人的历史积淀,然后加入个人的灵感,才能成事。但消化类似于搜索,这一步对于人及其有限的脑容量、记忆力和时间,挑战实在太大了。而人作为万物之灵,“灵感”其实人人都有。

现在好了,超大生成模型几乎无限延伸了人的搜索消化的能力,在很多细分领域甚至可以对历史做到一网打尽。深度学习的革命现在可以让消化了的东西重新符号化(包括语言符号、音频符号和视频符号),提供给人选择。这是千载难逢的绝佳组合:人只要判定就好了。判定是灵感参与的线性决策过程,n 挑 1 也就是 n 倍的判定,依然是线性的。对于文学艺术创作,这个太高效了。人类进入“艺术大爆炸”、“艺术个性化”时代,百花齐放,人人皆为艺术家,不是不可以想见的。

熟读唐诗三百首,以前是成为古典诗人的必由之路,被认为是必要的苦功夫。现如今,300 就是个笑话,整个古典诗词喂进模型去也是个小 case。总体而言,消化大数据,人比起机器,就好比蚂蚁比大象。


更何况不少创造性应用并不一定要专门针对艺术家或工匠的群体,有相当场景是普罗百姓都有需求的。例如应用文写作、秘书2.0, 编辑2.0, 确保出版插图永无侵权烦恼,等等等等。





推荐Chris Manning 论大模型,并附上相关讨论

李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

预告:李维等 《知识图谱:演进、技术和实践》(机械工业出版社 2022)




3. Trained model
During the training of the Teacher-Forced model, you provided the French sentences to the decoder as inputs. What are the decoder inputs when performing a new translation? You cannot provide the translation as an input because that’s what you want the model to generate.

4. Decoder of the inference model
You can solve this by building a recursive decoder which generates predictions for a single time step. First, it takes in some onehot encoded input word and some previous state as the initial state. The GRU layer then produces an output and a new state. Then, this GRU output goes through a Dense layer and produces an output word. In the next time step, the output and the new GRU state from the previous step become inputs to the decoder.

就是说在训练神经翻译模型的时候,源语与目标语都在,都作为 decoder 的 inputs 来帮助模型训练。但是到了应用模型来翻译的时候,目标语作为 input 的条件不在了,那么怎么来保证目标语的影响呢?

讲义中所谓 recursive decoder 就是把目标语的语言模型带入翻译过程。这时候与训练的时候有所不同,不再是具体的目标语的句子来制约,而是用目标语预测下一词的语言模型来介入。这就是为什么神经翻译可以通顺地道的主要原因。因为有了目标语语言模型的引导。

神经翻译常常通顺有余,精准不足。这其实是一对矛盾的反映,本质上是因为 decoder 解码的时候,有两股力量在影响它,一个是源语encoder 编码的输入(上下文的 vectors),另一个是目标语语言模型(next word prediction)的输入,这两个因素一个管精准,一个管通顺,协调起来难免出现偏差或偏向。


培训模型在“教师强制”模型的培训过程中,提供给译码器的法语句子作为输入。当执行新的翻译时,解码器的输入是什么?您不能将翻译作为输入来提供,因为这是您希望模型生成的结果。推理模型可以通过构建一个递归解码器来解决这个问题,该递归解码器可以生成针对 “单个时间步长“(即:词) 的预测。首先,它接受一个热编码的输入词(这是指的源语输入文句的词)和一些以前的状态(这是指该词的上文隐藏层的内部表示)作为初始状态。接着,GRU 神经层生成一个输出(指的是下一词的向量表示)和一个新状态。然后,这个 GRU 输出通过全连接稠密层产生一个输出词(这是目标语下一词)。在下一个时间步长中,前一步骤的输出(目标语的词)和新的 GRU 状态成为解码器的输入。


sos we love cats eos –> eos cats love we sos

这样一来,当 sos(句首标志)作为解码器启动的初始单元的时候,伴随它的前文(上下文中的上文部分)不再是空(没有逆序的原句,前文是空的),而是整个句子的浓缩状态来支持它。这就说明了为什么一个 sos 的启动操作,可以依据前文生成下一个词,与 we 对应的 nous,后面就是“链式反应”了,直到生成句末标志 eos,完成一个句子的翻译。在这个递归的链式反应的每一步,每生成一个词,“前文” 就少了一个词,前文的状态因此也在步步更新作为下一步的输入,而所生成的目标语词也作为下一步的输入带入了目标语的生成模型,二者的相互作用造成了精准和通顺的妥协和平衡。目前看来,目标语的生成模型对于翻译的作用容易大于前文状态的作用,造成错译(张冠李戴、指鹿为马,人间蒸发,鬼影乍现等等问题)。但原则上,应该有办法去做平衡配置,就好比在精准和召回之间做平衡配置一样。


11. Generating translations
Now you can start recursively generating French words. First you define a variable fr_sent to hold the full sentence. Then for fr_len steps in a loop you do the following. First you predict a word using the decoder. Remember that, the inputs to the decoder are, a word from the French vocabulary and the previous state of the decoder. In the fist step, the input will be "sos" and the input state will be the context vector from the encoder. This model then outputs a word, as a probability distribution and a new state. The new state will be recursively assigned to de_s_t. This means, at every time step, the previous decoder state will become an input to the model. Then in the next step you get the actual word string using probs2word() function. probs2word() is a function that accepts a probability distribution and a tokenizer and outputs the corresponding French word. After that, you convert that word to an onehot encoded sequence using the word2onehot() function. This is assigned back to de_seq which becomes an input to the model in the next step. And you keep iterating this process until the output word is "eos" or, until the end of the for loop.



11. 生成翻译
现在您可以开始递归生成法语译文了。首先,您定义一个变量 fr_sent 来保存完整的译文句子。然后对于循环中的 fr_len 步骤,您执行以下操作。首先,您使用解码器预测一个单词。请记住,解码器的输入是法语【即目标语】词汇表中的一个单词和解码器的先前状态【即源语的前文】。在第一步中,输入将是“sos”【句首标志】,输入的状态将是来自编码器的(源语文句的)上下文向量。然后该模型输出一个(目标语)单词【在目标语词汇表中的概率分布】和一个新状态【上下文动态更新】。新状态将递归赋值给 de_s_t。这意味着,在每个(单词生成的)时间步,之前的解码器状态都将成为模型的输入【这是译文精准的源头】。然后在下一步中,您使用 probs2word() 函数(从所预测的词的词汇概率分布)获取实际的单词字符串。probs2word() 是一个接受概率并输出相应法语单词的函数。之后,您使用 word2onehot() 函数将该单词转换为 onehot 编码序列【把生成的词重新编码以便模型下一步使用:这是引入目标语生成模型的关键输入,它是译文地道通顺的保证】。这将被分配回 de_seq,后者将在下一步中成为模型的输入。然后你不断地迭代这个过程,直到输出单词是“eos”【句末标志】,或者直到 for 循环结束。



Got It!
1. Wrap-up and the final showdown
You've learned a great deal about machine translation and maybe a little bit of French as well. Let's have a look back at what you've learned.

2. What you've done so far
First, in chapter 1 you learned what the encoder decoder architecture looks like and how it applies to machine translation. You then played around with a sequential model known as GRU, or, gated recurrent units. In chapter 2 you looked more closely at the encoder decoder architecture and implemented an actual encoder decoder model in Keras. You also learned how to use Dense and TimeDistributed layers in Keras to implement a prediction layer that outputs translation words.

3. What you've done so far
In chapter 3, you learned various data preprocessing techniques. You then trained an actual machine translation model and used it to generate translations. Finally in chapter 4 you learned about a training method known as "teacher forcing" which gives even better performance. You trained your own model using teacher forcing and then generated translations. At the end, you learned about word embeddings and how they can be incorporated to the machine translation model.

4. Machine transation models
In this course, you implemented three different models for an English to French translation task. Model 1 was the most basic model. The encoder consumed the English words as onehot encoded vectors and produced a context vector. Next the decoder consumed this context vector and produced the correct translation. In model 2, the encoder remained the same. The decoder in this model predicted the next word in the translation, given the previous words. For model 3, we replaced onehot vectors with word vectors. Word vectors are much more powerful than onehot vectors and enables the model to learn semantics of words. For example, word vectors capture that a cat is more similar to a dog than a window.

5. Performance of different models
Here you can see the performance of those three models. You can see that the models trained with teacher forcing give the best results. You should note that the model that uses word vectors gets to a higher accuracy much quicker than the model that is not using word vectors.

6. Latest developments and further reading
Though you used the accuracy to evaluate model performance, there is a better metric known as BLEU which tries to imitate how a human would assess the translation. Another important thing to know is how out-of-vocabulary words are treated in productionized models. For example, Google cannot simply replace unknown words with a special token. To address this problem these models use a word piece model. A word piece model will identify most frequent sub-words in a corpus. For example, if the model has seen the words "low" and "newer", and has learned the sub-words "low" and "er", the model can represent the unseen word "lower". One of the most important developments in the field of NLP is the Transformer. Transformer is based on the encoder-decoder architecture. However it does not use any sequential models like GRUs. Rather, it uses something known as attention which is more light-weight than a GRU.

7. All the best!
I hope this has been a fruitful journey about machine translation and I wish you all the best.

李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2022)

《李白126:神经 attention 机制搞定代词指代的案例》

李:看到 attention 机制一个图示:

这可算是 attention 机制可视化以后,明确显示其解决了 pronoun coreference 的难题。看前后两句 it 与 animal 和 street 的关联强度就明白了:

1. The <animal> didn’t cross the street because [it] was too tired.
2. The animal didn’t cross the <street> because [it] was too wide.

这只是过程中其中一幅 attention 的图示化,图中没有显示其他两两 attentions 的图示(包括 it/animal/street 与 wide/tired 的两两关联度),看完就知道形容词(wide/tired)与 host noun(animal/street)之间的相谐性,是如何对 it 的 coreference attention 的影响力了。

这种两两配对的机制 简直令人发指地有效 而且有解释性,也不怕爆炸,反正所谓 multihead self-attention 机制都是可以并行计算的,大不了多上GPU,费些电而已。


李:不知道 不过就看到的这个结果,已经让人嫉妒得咬牙。好玩的插曲是,下面留言貌似还有个“理呆”傻傻地问:老师 为什么 it 与不相干的词 wide 有很强的关系?这位学生理解了 it 与名词的关系 却不能理解与形容词的关系,哈。



李:感觉是 如果句子处理满足下列条件,能穷举两两关系 而且有足够数据训练去计算这种关系,那么我们引以为傲的结构,其桥梁价值就会趋近于零,因为位置信息加语义相谐的 attentions,应该可以搞定这种 hidden correlations。这样说来,attention is all we need 即便从字面上看 也说的不错。

自然语言说复杂也复杂 但说简单也简单。简单在于,有无穷无尽的语料,预训练可以发掘很多语言知识。到下游应用的时候 单位开始变小,小到一句一词 大也不过一篇文章 对于 attention,这都不算事。(也有人现在尝试把 input 扩大到一组文件,来做跨文件自动摘要,结果也让人开眼)。


李:可几年前,我们是不相信神经系统可以搞定 long distance(hidden) correlations 的,当时觉得非符号结构不能的。这个不服不行。 




李:是 he,不是 we。嗯,这两例的确没搞定,也更 tricky 一些,有间接宾语干扰项。再等两年,等最新机制和方法慢慢渗透消化部署到商用神经翻译系统后再看看搞定了多少。总之,总体方向上是向好的,我觉得。越来越多的“非低枝果实”正在被神经吞噬,一个幽灵在地球徘徊……





李维 郭进《自然语言处理答问》(商务印书馆 2020)

预告:李维《巴别塔影:符号自然语言处理之旅》(人民邮电出版社 2021)