How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs
Karin De Langis, Jong Inn Park, Andreas Schramm, Bin Hu, Khanh Chi Le, Dongyeop Kang
Abstract
Large language models (LLMs) exihibit increasingly sophisticated linguistic capabilities, yet the extent to which these behaviors reflect human-like cognition versus advanced pattern recognition remains an open question.In this study, we investigate how LLMs process the temporal meaning of linguistic aspect in narratives that were previously used in human studies. Using an Expert-in-the-Loop probing pipeline, we conduct a series of targeted experiments to assess whether LLMs construct semantic representations and pragmatic inferences in a human-like manner.Our findings show that LLMs over-rely on prototypicality, produce inconsistent aspectual judgments, and struggle with causal reasoning derived from aspect, raising concerns about their ability to fully comprehend narratives.These results suggest that LLMs process aspect fundamentally differently from humans and lack robust narrative understanding.Beyond these empirical findings, we develop a standardized experimental framework for the reliable assessment of LLMs’ cognitive and linguistic capabilities.- Anthology ID:
- 2025.acl-long.1415
- Volume:
- Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2025
- Address:
- Vienna, Austria
- Editors:
- Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 29174–29191
- Language:
- URL:
- https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1415/
- DOI:
- Cite (ACL):
- Karin De Langis, Jong Inn Park, Andreas Schramm, Bin Hu, Khanh Chi Le, and Dongyeop Kang. 2025. How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29174–29191, Vienna, Austria. Association for Computational Linguistics.
- Cite (Informal):
- How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs (De Langis et al., ACL 2025)
- PDF:
- https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1415.pdf