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    <abstract>We present a Large Language Model (LLM) based system for question answering (QA) over tabular data that leverages multi-turn prompting to automatically generate executable Pandas functions. Our framework decomposes the problem into three key steps: (1) Answer Type Identification, where the system identifies the expected format of the response (e.g., boolean, number, category); (2) Pandas Function Generation, which generates a corresponding Pandas function using table metadata and in-context examples, and (3) Error Correction and Regeneration, where iteratively refining the function based on error feedback from executions. Evaluations on the SemEval-2025 Task 8 Tabular QA benchmark (Grijalba et al., 2024) demonstrate that our multi-turn approach significantly outperforms single-turn prompting models in exact match accuracy by 7.3%. The proposed system not only improves code generation robustness but also paves the way for enhanced and adaptability in table-QA reasoning tasks. Our implementation is available at https://github.com/Gyyz/Question_Answering-over-Tabular-Data.</abstract>
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%0 Conference Proceedings
%T I2R-NLP at SemEval-2025 Task 8: Question Answering on Tabular Data
%A Gao, Yuze
%A Chen, Bin
%A Su, Jian
%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 gao-etal-2025-i2r
%X We present a Large Language Model (LLM) based system for question answering (QA) over tabular data that leverages multi-turn prompting to automatically generate executable Pandas functions. Our framework decomposes the problem into three key steps: (1) Answer Type Identification, where the system identifies the expected format of the response (e.g., boolean, number, category); (2) Pandas Function Generation, which generates a corresponding Pandas function using table metadata and in-context examples, and (3) Error Correction and Regeneration, where iteratively refining the function based on error feedback from executions. Evaluations on the SemEval-2025 Task 8 Tabular QA benchmark (Grijalba et al., 2024) demonstrate that our multi-turn approach significantly outperforms single-turn prompting models in exact match accuracy by 7.3%. The proposed system not only improves code generation robustness but also paves the way for enhanced and adaptability in table-QA reasoning tasks. Our implementation is available at https://github.com/Gyyz/Question_Answering-over-Tabular-Data.
%U https://aclanthology.org/2025.semeval-1.14/
%P 90-101

Markdown (Informal)

[I2R-NLP at SemEval-2025 Task 8: Question Answering on Tabular Data](https://aclanthology.org/2025.semeval-1.14/) (Gao et al., SemEval 2025)

• I2R-NLP at SemEval-2025 Task 8: Question Answering on Tabular Data (Gao et al., SemEval 2025)

ACL

• Yuze Gao, Bin Chen, and Jian Su. 2025. I2R-NLP at SemEval-2025 Task 8: Question Answering on Tabular Data. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 90–101, Vienna, Austria. Association for Computational Linguistics.