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    <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 benchmark specifically designed for tabular temporal question answering. We provide critical insights for enhancing 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 improves evidence-based reasoning across various models. Additionally, our experimental results reveal that indirect supervision with auxiliary unstructured data (TRAM) 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.</abstract>
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%0 Conference Proceedings
%T Enhancing Temporal Understanding in LLMs for Semi-structured Tables
%A Deng, Irwin
%A Dixit, Kushagra
%A Roth, Dan
%A Gupta, Vivek
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F deng-etal-2025-enhancing
%X 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 benchmark specifically designed for tabular temporal question answering. We provide critical insights for enhancing 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 improves evidence-based reasoning across various models. Additionally, our experimental results reveal that indirect supervision with auxiliary unstructured data (TRAM) 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.
%R 10.18653/v1/2025.findings-naacl.278
%U https://aclanthology.org/2025.findings-naacl.278/
%U https://doi.org/10.18653/v1/2025.findings-naacl.278
%P 4936-4955

Markdown (Informal)

[Enhancing Temporal Understanding in LLMs for Semi-structured Tables](https://aclanthology.org/2025.findings-naacl.278/) (Deng et al., Findings 2025)

• Enhancing Temporal Understanding in LLMs for Semi-structured Tables (Deng et al., Findings 2025)

ACL

• Irwin Deng, Kushagra Dixit, Dan Roth, and Vivek Gupta. 2025. Enhancing Temporal Understanding in LLMs for Semi-structured Tables. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 4936–4955, Albuquerque, New Mexico. Association for Computational Linguistics.