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    <abstract>Tabular Question Answering (QA) is crucial for enabling automated reasoning over structured data, facilitating efficient information retrieval and decision-making across domains like finance, healthcare, and scientific research. This paper describes our system for the SemEval 2025 Task 8 on Question Answering over Tabular Data, specifically focusing on the DataBench QA and DataBench Lite QA subtasks. Our approach involves generating Python code using Large Language Models (LLMs) to extract answers from tabular data in a zero-shot setting. We investigate both multi-step Chain-of-Thought (CoT) and unified LLM approaches, where the latter demonstrates superior performance by minimizing error propagation and enhancing system stability. Our system prioritizes computational efficiency and scalability by minimizing the input data provided to the LLM, optimizing its ability to contextualize information effectively. We achieve this by sampling a minimal set of rows from the dataset and utilizing external execution with Python and Pandas to maintain efficiency. Our system achieved the highest accuracy amongst all small open-source models, ranking 1st in both subtasks.</abstract>
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%0 Conference Proceedings
%T ScottyPoseidon at SemEval-2025 Task 8: LLM-Driven Code Generation for Zero-Shot Question Answering on Tabular Data
%A R, Raghav
%A Vemali, Adarsh Prakash
%A Aswal, Darpan
%A Ramesh, Rahul
%A Bhupal, Ayush
%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 r-etal-2025-scottyposeidon
%X Tabular Question Answering (QA) is crucial for enabling automated reasoning over structured data, facilitating efficient information retrieval and decision-making across domains like finance, healthcare, and scientific research. This paper describes our system for the SemEval 2025 Task 8 on Question Answering over Tabular Data, specifically focusing on the DataBench QA and DataBench Lite QA subtasks. Our approach involves generating Python code using Large Language Models (LLMs) to extract answers from tabular data in a zero-shot setting. We investigate both multi-step Chain-of-Thought (CoT) and unified LLM approaches, where the latter demonstrates superior performance by minimizing error propagation and enhancing system stability. Our system prioritizes computational efficiency and scalability by minimizing the input data provided to the LLM, optimizing its ability to contextualize information effectively. We achieve this by sampling a minimal set of rows from the dataset and utilizing external execution with Python and Pandas to maintain efficiency. Our system achieved the highest accuracy amongst all small open-source models, ranking 1st in both subtasks.
%U https://aclanthology.org/2025.semeval-1.285/
%P 2197-2204

Markdown (Informal)

[ScottyPoseidon at SemEval-2025 Task 8: LLM-Driven Code Generation for Zero-Shot Question Answering on Tabular Data](https://aclanthology.org/2025.semeval-1.285/) (R et al., SemEval 2025)

• ScottyPoseidon at SemEval-2025 Task 8: LLM-Driven Code Generation for Zero-Shot Question Answering on Tabular Data (R et al., SemEval 2025)

ACL

• Raghav R, Adarsh Prakash Vemali, Darpan Aswal, Rahul Ramesh, and Ayush Bhupal. 2025. ScottyPoseidon at SemEval-2025 Task 8: LLM-Driven Code Generation for Zero-Shot Question Answering on Tabular Data. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2197–2204, Vienna, Austria. Association for Computational Linguistics.