《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)………

LLM(“老流氓”)做辅导最大的问题还是他不懂装懂,胡说八道。就是说他“耍流氓”的时候他不露声色,还是一副道貌岸然的样子。这个问题目前看来无解,就是说很难避免它在辅导你知识的同时,不夹带私货或者误导你。这也不能怪他,无所不知而且无穷耐心与天马行空胡说八道是伴生品,非专家无法区分。而被辅导者通常不是专家,否则为何要被辅导。

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。

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Wei:
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].

chat:
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:

chat: 
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?

 
chat:

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

chat:
注意力机制是神经网络中一种用于在处理输入时选择性关注某些部分的方法。注意力机制允许模型对输入的不同部分进行重要性评估,并专注于对当前任务更相关的部分。

在Transformer架构中,注意力机制用于在输入和输出序列之间建立全局依赖关系。注意力机制的工作原理是计算输入序列中每个位置的注意力分数,并使用这些分数在生成输出序列时重要性评估每个位置。

注意力机制可以被认为是一种函数,它接受一个查询、一个键和一个值,并返回一个加权的值和,其中加权由查询和键之间的相似性确定。在Transformer架构中,查询、键和值都由相同的一组学习参数表示,这称为自我注意。注意力机制也可以扩展到多个头,其中多组查询、键、值被学习,然后被拼接成最终输出,这称为多头注意。

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?

chat:

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 

chat:
Transformer架构中的多头注意力机制是用来抵消由平均注意力加权位置引起的有效分辨率降低的。使用多头注意力,多组注意权值被学习,然后被拼接在一起形成最终输出。

通过使用多个注意头,模型能够关注输入序列的不同部分并学习不同的表示。然后可以将这些不同的表示组合起来形成对输入序列的更全面的理解。多个头也允许模型学习输入和输出序列之间更复杂的关系。

此外,多头注意力机制允许模型同时关注输入序列的不同部分。在Transformer中,注意机制应用于整个输入序列,这使得模型能够在输入和输出序列之间建立全局依赖关系。使用多头注意力,模型可以并行关注输入序列的不同部分,进一步提高了模型在输入和输出序列之间学习依赖关系的能力。

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?

chat:

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
 
chat:

之间建立依赖关系了,为什么还要使用多头?

其实单个注意力机制是可以在任意两个tokens之间建立依赖关系的,但是当我们需要同时考虑多种依赖关系时,单个注意力机制就可能不能满足需求。这个时候就要用到多头注意力机制。

例如,当我们需要翻译一句话时,我们可能需要同时考虑这句话中词与词之间的关系,词与句子整体意思之间的关系以及句子中主语与谓语之间的关系。而多头注意力机制就可以通过多组不同的注意头来学习这些不同的依赖关系,然后将这些依赖关系组合起来形成一个更全面的理解。这样就可以在保证翻译质量的同时提高翻译的准确性和全面性。

总之,多头注意力机制是为了能够同时考虑多种依赖关系,从而提高模型的准确性和全面性。

Wei:
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。

chat:
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。

还有,在自然语言处理中,任务往往是非常复杂的,需要考虑各种各样的关系。而在这样的情况下,多头注意力机制可以帮助模型更好的学习这些关系,从而提高模型的准确性和全面性。

总之,多头注意力机制在Transformer模型中是有意义的,它可以帮助模型更好的学习全局依赖关系,提高模型的准确性和全面性,并且在实

Wei:
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?

chat: 
是的,您说得很对。在Transformer模型中,多头注意力机制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,这些头会与其他token的多个变体的向量表示两两计算关系的权重,以此来学习输入序列的不同部分之间的关系。这样的话,模型就可以同时关注序列中多个位置的信息,进而更好的学习全局依赖关系。

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?

chat:
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:
 
chat:
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?

 
chat:

自回归(auto-regression)是指一种模型,它在生成输出序列的每一步都会使用之前生成的输出作为输入来生成下一步的输出。这种模型通常用于时间序列预测和序列生成等问题。

例如,在文本生成中,自回归模型会依次生成每个单词,并在生成每个单词时使用之前生成的单词作为输入。这样,模型就可以了解之前的语境,从而生成更合理的文本。

Wei: 全联接层中的 point-wise 是个什么鬼?

chat:

“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?

chat:

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。

前馈网络是一种常见的神经网络结构,它将输入传递给一系列层,每层都会对输入进行转换,最后输出一个预测。全连接层是前馈网络中一种常见的层类型,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。

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。

总结来说,全连接层是前馈网络中的一种层类型,它可以和其他层类型组合在一起使用,而前馈网络则是一种常见的神经网络结构。

Wei: 编码器与解码器是如何对接的?

chat:

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。

编码器和解码器之间的对接方式是通过在解码器中加入了一个额外的子层来实现的,这个子层执行多头注意力,并在编码器堆栈输出上进行操作。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

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?

chat:
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。

需要注意的是,这个六层的选择是在论文中的实验中得出的结论,并不是固定不变的,对于其他任务和数据集,可能需要不同层数来获得更好的结果。

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?

chat:
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。

增加解码器层次可以带来更多的能力来捕获更高级别的语言结构,这样能够更好地处理复杂的文本。但是需要注意的是,增加层数会增加模型的复杂度,也会增加训练和预测的时间。

减少解码器层次可以降低模型的复杂度,提高训练和预测的时间,但是会减少模型捕获高级别语言结构的能力。

需要根据具体的任务和数据集来调整编码器和解码器的层数,来获得最佳的结果。

Wei:
为什么这个架构成为AI的通用结构,无论是生成任务还是辨识任务,现在的大模型都用它呢?

chat:
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

chat:
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?

chat:

图像在原始形式下不是序列数据,但是我们可以将其转换为序列数据来处理。

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。

Wei:
关于阅读理解上述论文,如果你是教授,你会用什么样的问题问学生,你期望得到什么样的回答,请用简明易懂的英语和中文来准备问题和答案:

chat: 

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
 

chat:

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 

这种方法在实际中非常高效,因为它可以使用高度优化的矩阵乘法代码来实现。

发布者

liweinlp

立委博士,计算语言学家,多语言多领域自然语言处理(NLP)资深架构师。Trend 首席科学家,聚焦医疗领域病友社区的媒体挖掘。前弘玑首席科学家,聚焦RPA+AI的NLP低代码多领域落地,设计NLP核心引擎雕龙,落地多领域场景,包括金融、电力、航空、水利、客服等。前讯飞AI研究院副院长,研发支持对话的多语言平台,前京东主任科学家, 主攻深度解析和知识图谱及其应用。Netbase前首席科学家,期间指挥研发了18种语言的理解和应用系统。特别是汉语和英语,具有世界一流的解析(parsing)精度,并且做到鲁棒、线速,scale up to 大数据,语义落地到数据挖掘和问答产品。Cymfony前研发副总,曾荣获第一届问答系统第一名(TREC-8 QA Track),并赢得17个小企业创新研究的信息抽取项目(PI for 17 SBIRs)。立委NLP工作的应用方向包括大数据舆情挖掘、客户情报、信息抽取、知识图谱、问答系统、智能助理、语义搜索等等。

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