@inproceedings{bansal-etal-2025-tablewise,
title = "{T}able{W}ise at {S}em{E}val-2025 Task 8: {LLM} Agents for {T}ab{QA}",
author = "Bansal, Harsh and
Raj, Aman and
Sharma, Akshit and
Krishnamurthy, Parameswari",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.87/",
pages = "623--626",
ISBN = "979-8-89176-273-2",
abstract = "Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering."
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<abstract>Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering.</abstract>
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%0 Conference Proceedings
%T TableWise at SemEval-2025 Task 8: LLM Agents for TabQA
%A Bansal, Harsh
%A Raj, Aman
%A Sharma, Akshit
%A Krishnamurthy, Parameswari
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F bansal-etal-2025-tablewise
%X Tabular Question Answering (TabQA) is a challenging task that requires models to comprehend structured tabular data and generate accurate responses based on complex reasoning. In this paper, we present our approach to SemEval Task 8: Tabular Question Answering, where we develop a large language model (LLM)-based agent capable of understanding and reasoning over tabular inputs. Our agent leverages a hybrid retrieval and generation strategy, incorporating structured table parsing, semantic understanding, and reasoning mechanisms to enhance response accuracy. We fine-tune a pre-trained LLM on domain-specific tabular data, integrating chain-of-thought prompting and adaptive decoding to improve multi-hop reasoning over tables. Experimental results demonstrate that our approach achieves competitive performance, effectively handling numerical operations, entity linking, and logical inference. Our findings suggest that LLM-based agents, when properly adapted, can significantly advance the state of the art in tabular question answering.
%U https://aclanthology.org/2025.semeval-1.87/
%P 623-626
Markdown (Informal)
[TableWise at SemEval-2025 Task 8: LLM Agents for TabQA](https://aclanthology.org/2025.semeval-1.87/) (Bansal et al., SemEval 2025)
ACL
- Harsh Bansal, Aman Raj, Akshit Sharma, and Parameswari Krishnamurthy. 2025. TableWise at SemEval-2025 Task 8: LLM Agents for TabQA. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 623–626, Vienna, Austria. Association for Computational Linguistics.