How can large language models become more human?
Daphne Wang, Mehrnoosh Sadrzadeh, Miloš Stanojević, Wing-Yee Chow, Richard Breheny
Abstract
Psycholinguistic experiments reveal that efficiency of human language use is founded on predictions at both syntactic and lexical levels. Previous models of human prediction exploiting LLMs have used an information theoretic measure called surprisal, with success on naturalistic text in a wide variety of languages, but under-performance on challenging text such as garden path sentences. This paper introduces a novel framework that combines the lexical predictions of an LLM with the syntactic structures provided by a dependency parser. The framework gives rise to an Incompatibility Fraction. When tested on two garden path datasets, it correlated well with human reading times, distinguished between easy and hard garden path, and outperformed surprisal.- Anthology ID:
- 2024.cmcl-1.14
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Tatsuki Kuribayashi, Giulia Rambelli, Ece Takmaz, Philipp Wicke, Yohei Oseki
- Venues:
- CMCL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 166–176
- Language:
- URL:
- https://aclanthology.org/2024.cmcl-1.14
- DOI:
- Cite (ACL):
- Daphne Wang, Mehrnoosh Sadrzadeh, Miloš Stanojević, Wing-Yee Chow, and Richard Breheny. 2024. How can large language models become more human?. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 166–176, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- How can large language models become more human? (Wang et al., CMCL-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.cmcl-1.14.pdf