神经语言模型中抽象相似关系的计算是类人的吗？
Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?
摘要：
近年来，深度神经语言模型在各种NLP任务中取得了长足的进步。这项工作探讨了一个问题的一个方面，即最先进的NLP模型是否表现出人类认知的基本机制。探索的重点是一个相对原始的机制，有很多证据，从各种心理语言学实验的婴儿。摘要“抽象同一关系”的计算在人类语言习得和处理过程中，特别是在学习更复杂的语法规则时，起着重要的作用。为了在BERT和其他预训练语言模型（PLM）中研究这种机制，以婴儿研究的实验设计为起点。在此基础上，我们设计了实验设置，其中每个元素从原来的研究被映射到一个组成部分的语言模型。即使在我们的实验中的任务是相对简单的，结果表明，计算抽象的相同关系的认知能力是强于在所有调查的PLM婴儿。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
