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    <abstract>Table Question Answering (TQA) involves extracting answers from structured data using natural language queries, a challenging task due to diverse table formats and complex reasoning. This work develops a TQA system using the DataBench dataset, leveraging large language models (LLMs) to generate Python code in a zero-shot manner. Our approach is highly generic, relying on a structured Chain-of-Thought framework to improve reasoning and data interpretation. Experimental results demonstrate that our method achieves high accuracy and efficiency, making it a flexible and effective solution for real-world tabular question answering.</abstract>
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%0 Conference Proceedings
%T AlexNLP-MO at SemEval-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data
%A Mokhtar, Omar
%A Ghanem, Minah
%A El - Makky, Nagwa
%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 mokhtar-etal-2025-alexnlp
%X Table Question Answering (TQA) involves extracting answers from structured data using natural language queries, a challenging task due to diverse table formats and complex reasoning. This work develops a TQA system using the DataBench dataset, leveraging large language models (LLMs) to generate Python code in a zero-shot manner. Our approach is highly generic, relying on a structured Chain-of-Thought framework to improve reasoning and data interpretation. Experimental results demonstrate that our method achieves high accuracy and efficiency, making it a flexible and effective solution for real-world tabular question answering.
%U https://aclanthology.org/2025.semeval-1.118/
%P 865-873

Markdown (Informal)

[AlexNLP-MO at SemEval-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data](https://aclanthology.org/2025.semeval-1.118/) (Mokhtar et al., SemEval 2025)

• AlexNLP-MO at SemEval-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data (Mokhtar et al., SemEval 2025)

ACL

• Omar Mokhtar, Minah Ghanem, and Nagwa El - Makky. 2025. AlexNLP-MO at SemEval-2025 Task 8: A Chain of Thought Framework for Question-Answering over Tabular Data. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 865–873, Vienna, Austria. Association for Computational Linguistics.