Abstract
The ability to transmit and receive complex information via language is unique to humans and is the basis of traditions, culture and versatile social interactions. Through the disruptive introduction of transformer based large language models (LLMs) humans are not the only entity to "understand" and produce language any more. In the present study, we have performed the first steps to use LLMs as a model to understand fundamental mechanisms of language processing in neural networks, in order to make predictions and generate hypotheses on how the human brain does language processing. Thus, we have used ChatGPT to generate seven different stylistic variations of ten different narratives (Aesop's fables). We used these stories as input for the open source LLM BERT and have analyzed the activation patterns of the hidden units of BERT using multi-dimensional scaling and cluster analysis. We found that the activation vectors of the hidden units cluster according to stylistic variations in earlier layers of BERT (1) than narrative content (4-5). Despite the fact that BERT consists of 12 identical building blocks that are stacked and trained on large text corpora, the different layers perform different tasks. This is a very useful model of the human brain, where self-similar structures, i.e. different areas of the cerebral cortex, can have different functions and are therefore well suited to processing language in a very efficient way. The proposed approach has the potential to open the black box of LLMs on the one hand, and might be a further step to unravel the neural processes underlying human language processing and cognition in general.
Abstract (translated)
通过语言进行复杂信息传输和接收是人类独有的能力，也是传统、文化和多才多艺的社会互动的基础。通过颠覆性的基于Transformer的大型语言模型（LLMs）引入，人类不再是唯一能够理解和产生语言的实体。在当前的研究中，我们使用了LLMs作为模型来理解神经网络中语言处理的基本机制，以进行预测和生成关于人类大脑如何进行语言处理的研究。因此，我们使用ChatGPT生成了10个不同叙事的7种不同风格。我们将这些故事作为输入传送到开源LLM BERT，并使用多维缩放和聚类分析来分析BERT隐藏层单元的激活模式。我们发现，隐藏单元的激活矢量根据BERT早期层风格的stylistic variations（1）比故事内容（4-5）更加聚类。尽管BERT由12个相同的构建模块组成，这些不同的层执行不同的任务。这是一个非常有用的描述人类大脑的模型，因为自我相似结构（即不同的大脑皮层区域）可以具有不同的功能，因此非常适于以非常高效的方式处理语言。所提出的方法有望打开LLMs的黑盒，同时也许是一个进一步揭开人类语言处理和认知的神经过程的步骤。
URL
https://arxiv.org/abs/2405.02024
