Abstract
Unraveling the intricate details of events in natural language necessitates a subtle understanding of temporal dynamics. Despite the adeptness of Large Language Models (LLMs) in discerning patterns and relationships from data, their inherent comprehension of temporal dynamics remains a formidable challenge. This research meticulously explores these intrinsic challenges within LLMs, with a specific emphasis on evaluating the performance of GPT-3.5 and GPT-4 models in the analysis of temporal data. Employing two distinct prompt types, namely Question Answering (QA) format and Textual Entailment (TE) format, our analysis probes into both implicit and explicit events. The findings underscore noteworthy trends, revealing disparities in the performance of GPT-3.5 and GPT-4. Notably, biases toward specific temporal relationships come to light, with GPT-3.5 demonstrating a preference for “AFTER” in the QA format for both implicit and explicit events, while GPT-4 leans towards “BEFORE”. Furthermore, a consistent pattern surfaces wherein GPT-3.5 tends towards “TRUE”, and GPT-4 exhibits a preference for “FALSE” in the TE format for both implicit and explicit events. This persistent discrepancy between GPT-3.5 and GPT-4 in handling temporal data highlights the intricate nature of inductive bias in LLMs, suggesting that the evolution of these models may not merely mitigate bias but may introduce new layers of complexity.- Anthology ID:
- 2024.naacl-short.20
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 220–228
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.20
- DOI:
- Cite (ACL):
- Sindhu Kishore and Hangfeng He. 2024. Unveiling Divergent Inductive Biases of LLMs on Temporal Data. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 220–228, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Unveiling Divergent Inductive Biases of LLMs on Temporal Data (Kishore & He, NAACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-short.20.pdf