Enhancing Temporal Understanding in LLMs for Semi-structured Tables

• URL: http://arxiv.org/abs/2407.16030v1
• Date: Mon, 22 Jul 2024 20:13:10 GMT
• Title: Enhancing Temporal Understanding in LLMs for Semi-structured Tables
• Authors: Irwin Deng, Kushagra Dixit, Vivek Gupta, Dan Roth,
• Abstract summary: We conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of large language models (LLMs) Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for temporal temporal question answering. We introduce a novel approach, C.L.E.A.R. to strengthen LLM capabilities in this domain.
• Score: 50.59009084277447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal reasoning over tabular data presents substantial challenges for large language models (LLMs), as evidenced by recent research. In this study, we conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of LLMs. Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for tabular temporal question answering. We provide critical insights for improving LLM performance in temporal reasoning tasks with tabular data. Furthermore, we introduce a novel approach, C.L.E.A.R to strengthen LLM capabilities in this domain. Our findings demonstrate that our method significantly improves evidence-based reasoning across various models. Additionally, our experimental results reveal that indirect supervision with auxiliary data substantially boosts model performance in these tasks. This work contributes to a deeper understanding of LLMs' temporal reasoning abilities over tabular data and promotes advancements in their application across diverse fields.

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