论文标题
大脑和语言模型中语言特性的联合处理
Joint processing of linguistic properties in brains and language models
论文作者
论文摘要
语言模型已被证明在预测经历复杂语言刺激的受试者的大脑记录方面非常有效。为了深入了解这种对齐方式，重要的是要了解人脑与语言模型对语言信息的详细处理之间的对应关系。我们通过直接方法调查了这种对应关系，在该方法中，我们消除了与语言模型表示中特定语言特性有关的信息，并观察该干预措施如何影响与参与者聆听故事时获得的fMRI脑记录的对齐。我们研究了一系列语言特性（表面，句法和语义），发现消除每种特性会导致大脑比对显着降低。具体而言，我们发现句法特性（即顶部成分和树深度）对跨模型层的脑对齐趋势具有最大的影响。这些发现为特定语言信息在大脑和语言模型之间的对齐中的作用提供了明确的证据，并开放了新的途径，用于映射两个系统中的联合信息处理。我们将代码公开提供[https://github.com/subbareddy248/linguistic-properties-brain-anignment]。
Language models have been shown to be very effective in predicting brain recordings of subjects experiencing complex language stimuli. For a deeper understanding of this alignment, it is important to understand the correspondence between the detailed processing of linguistic information by the human brain versus language models. We investigate this correspondence via a direct approach, in which we eliminate information related to specific linguistic properties in the language model representations and observe how this intervention affects the alignment with fMRI brain recordings obtained while participants listened to a story. We investigate a range of linguistic properties (surface, syntactic, and semantic) and find that the elimination of each one results in a significant decrease in brain alignment. Specifically, we find that syntactic properties (i.e. Top Constituents and Tree Depth) have the largest effect on the trend of brain alignment across model layers. These findings provide clear evidence for the role of specific linguistic information in the alignment between brain and language models, and open new avenues for mapping the joint information processing in both systems. We make the code publicly available [https://github.com/subbareddy248/linguistic-properties-brain-alignment].
