Leveraging Human Production-Interpretation Asymmetries to Test LLM Cognitive Plausibility

• URL: http://arxiv.org/abs/2503.17579v1
• Date: Fri, 21 Mar 2025 23:25:42 GMT
• Title: Leveraging Human Production-Interpretation Asymmetries to Test LLM Cognitive Plausibility
• Authors: Suet-Ying Lam, Qingcheng Zeng, Jingyi Wu, Rob Voigt,
• Abstract summary: We examine whether large language models process language similarly to humans.<n>We find that some LLMs do quantitatively and qualitatively reflect human-like asymmetries between production and interpretation.
• Score: 7.183662547358301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whether large language models (LLMs) process language similarly to humans has been the subject of much theoretical and practical debate. We examine this question through the lens of the production-interpretation distinction found in human sentence processing and evaluate the extent to which instruction-tuned LLMs replicate this distinction. Using an empirically documented asymmetry between production and interpretation in humans for implicit causality verbs as a testbed, we find that some LLMs do quantitatively and qualitatively reflect human-like asymmetries between production and interpretation. We demonstrate that whether this behavior holds depends upon both model size - with larger models more likely to reflect human-like patterns and the choice of meta-linguistic prompts used to elicit the behavior.

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