Papers by Kushagra Dixit
LLM-Symbolic Integration for Robust Temporal Tabular Reasoning (2025.findings-acl)
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| Challenge: | Existing methods for temporal tabular question answering are inconsistent and fail to provide the variability needed to thoroughly evaluate models. |
| Approach: | TEMPTABQA-C uses a synthetic dataset and symbolic representation to generate and execute SQL queries. |
| Outcome: | TEMPTABQA-C improves on previous methods for temporal tabular question answering . incorporating adaptive fewshot prompting with tailored examples improves performance . lack of robustness, scalability, and interpretable solutions is key obstacle . |
Enhancing Temporal Understanding in LLMs for Semi-structured Tables (2025.findings-naacl)
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| Challenge: | Temporal reasoning over tabular data presents significant challenges for large language models (LLMs), as evidenced by recent research. |
| Approach: | They propose a method that enhances LLMs' temporal reasoning over tabular data by using standard prompts and introduce a novel approach, C.L.E.A.R. |
| Outcome: | The proposed method improves evidence-based reasoning across models and indirect supervision with auxiliary unstructured data significantly boosts model performance in these tasks. |