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    <abstract>Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data—a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TEMPTABQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive fewshot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs. Code and TEMPTABQA-C dataset: https:// coral-lab-asu.github.io/llm_symbolic.</abstract>
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%0 Conference Proceedings
%T LLM-Symbolic Integration for Robust Temporal Tabular Reasoning
%A Kulkarni, Atharv
%A Dixit, Kushagra
%A Srikumar, Vivek
%A Roth, Dan
%A Gupta, Vivek
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F kulkarni-etal-2025-llm
%X Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data—a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TEMPTABQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive fewshot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs. Code and TEMPTABQA-C dataset: https:// coral-lab-asu.github.io/llm_symbolic.
%R 10.18653/v1/2025.findings-acl.1022
%U https://aclanthology.org/2025.findings-acl.1022/
%U https://doi.org/10.18653/v1/2025.findings-acl.1022
%P 19914-19940

Markdown (Informal)

[LLM-Symbolic Integration for Robust Temporal Tabular Reasoning](https://aclanthology.org/2025.findings-acl.1022/) (Kulkarni et al., Findings 2025)

• LLM-Symbolic Integration for Robust Temporal Tabular Reasoning (Kulkarni et al., Findings 2025)

ACL

• Atharv Kulkarni, Kushagra Dixit, Vivek Srikumar, Dan Roth, and Vivek Gupta. 2025. LLM-Symbolic Integration for Robust Temporal Tabular Reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19914–19940, Vienna, Austria. Association for Computational Linguistics.