Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability

• URL: http://arxiv.org/abs/2409.15827v1
• Date: Tue, 24 Sep 2024 07:40:33 GMT
• Title: Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability
• Authors: Xufeng Duan, Xinyu Zhou, Bei Xiao, Zhenguang G. Cai,
• Abstract summary: This study employs psycholinguistic paradigms to explore neuron-level representations in language model across three tasks. Our findings indicate that while GPT-2-XL struggles with the sound-shape task, it demonstrates human-like abilities in both sound-gender association and implicit causality.
• Score: 2.672177830116334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) become advance in their linguistic capacity, understanding how they capture aspects of language competence remains a significant challenge. This study therefore employs psycholinguistic paradigms, which are well-suited for probing deeper cognitive aspects of language processing, to explore neuron-level representations in language model across three tasks: sound-shape association, sound-gender association, and implicit causality. Our findings indicate that while GPT-2-XL struggles with the sound-shape task, it demonstrates human-like abilities in both sound-gender association and implicit causality. Targeted neuron ablation and activation manipulation reveal a crucial relationship: when GPT-2-XL displays a linguistic ability, specific neurons correspond to that competence; conversely, the absence of such an ability indicates a lack of specialized neurons. This study is the first to utilize psycholinguistic experiments to investigate deep language competence at the neuron level, providing a new level of granularity in model interpretability and insights into the internal mechanisms driving language ability in transformer based LLMs.

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