论文标题
在神经语言模型中，抽象相同关系的计算是否类似于人类？
Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?
论文作者
论文摘要
近年来，深度神经语言模型在各种NLP任务中取得了巨大进步。这项工作探讨了一个问题的一个方面，最先进的NLP模型是否表现出从人类认知中知道的基本机制。该探索集中在一种相对原始的机制上，该机制有很多来自婴儿的心理语言实验的证据。假定“抽象相同关系”的计算在人类语言获取和处理中起着重要作用，尤其是在学习更复杂的语法规则中。为了在BERT和其他预训练的语言模型（PLM）中研究这种机制，以婴儿研究为起点。在此基础上，我们设计了实验设置，其中将原始研究的每个元素映射到语言模型的组成部分。尽管我们的实验中的任务相对简单，但结果表明，在婴儿中，计算抽象相同关系的认知能力比所有研究的PLM都更强。
In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot of evidence from various psycholinguistic experiments with infants. The computation of "abstract sameness relations" is assumed to play an important role in human language acquisition and processing, especially in learning more complex grammar rules. In order to investigate this mechanism in BERT and other pre-trained language models (PLMs), the experiment designs from studies with infants were taken as the starting point. On this basis, we designed experimental settings in which each element from the original studies was mapped to a component of language models. Even though the task in our experiments was relatively simple, the results suggest that the cognitive faculty of computing abstract sameness relations is stronger in infants than in all investigated PLMs.
