Year: 2025
Type: conference-paper
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 pronoun 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.Our codes and results are available here.
Authors: Suet-Ying Lam, Qingcheng Zeng, Jingyi Wu, Rob Voigt
Institution: Northwestern University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889552