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    <abstract>This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradigms: psycholinguistic and neurolinguistic. Traditional psycholinguistic evaluations often reflect statistical rules that may not accurately represent LLMs’ true linguistic competence. We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. This method allows for a detailed examination of how LLMs represent form and meaning, and whether these representations are consistent across languages. We found: (1) Psycholinguistic and neurolinguistic methods reveal that language performance and competence are distinct; (2) Direct probability measurement may not accurately assess linguistic competence; (3) Instruction tuning won’t change much competence but improve performance; (4) LLMs exhibit higher competence and performance in form compared to meaning. Additionally, we introduce new conceptual minimal pair datasets for Chinese (COMPS-ZH) and German (COMPS-DE), complementing existing English datasets.</abstract>
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%0 Conference Proceedings
%T Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence
%A He, Linyang
%A Nie, Ercong
%A Schmid, Helmut
%A Schuetze, Hinrich
%A Mesgarani, Nima
%A Brennan, Jonathan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F he-etal-2025-large-language
%X This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment paradigms: psycholinguistic and neurolinguistic. Traditional psycholinguistic evaluations often reflect statistical rules that may not accurately represent LLMs’ true linguistic competence. We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. This method allows for a detailed examination of how LLMs represent form and meaning, and whether these representations are consistent across languages. We found: (1) Psycholinguistic and neurolinguistic methods reveal that language performance and competence are distinct; (2) Direct probability measurement may not accurately assess linguistic competence; (3) Instruction tuning won’t change much competence but improve performance; (4) LLMs exhibit higher competence and performance in form compared to meaning. Additionally, we introduce new conceptual minimal pair datasets for Chinese (COMPS-ZH) and German (COMPS-DE), complementing existing English datasets.
%R 10.18653/v1/2025.findings-acl.986
%U https://aclanthology.org/2025.findings-acl.986/
%U https://doi.org/10.18653/v1/2025.findings-acl.986
%P 19284-19302

Markdown (Informal)

[Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence](https://aclanthology.org/2025.findings-acl.986/) (He et al., Findings 2025)

• Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence (He et al., Findings 2025)

ACL

• Linyang He, Ercong Nie, Helmut Schmid, Hinrich Schuetze, Nima Mesgarani, and Jonathan Brennan. 2025. Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19284–19302, Vienna, Austria. Association for Computational Linguistics.