AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question Answering
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Abstract (arXiv)
In this paper, we present our submission to SemEval-2025 Task 8: Question Answering over Tabular Data. This task, evaluated on the DataBench dataset, assesses Large Language Models' (LLMs) ability to answer natural language questions over structured data while addressing topic diversity and table size limitations in previous benchmarks. We propose a system that employs effective LLM prompting to translate natural language queries into executable code, enabling accurate responses, error correction, and interpretability. Our approach ranks first in both subtasks of the competition in the proprietary model category, significantly outperforming the organizer's baseline.
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References · 12
- 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
- 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
- 2022Binding Language Models in Symbolic Languagescited by 228
- 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 178
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
- 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
- 2023ReAcTable: Enhancing ReAct for Table Question Answeringcited by 76
- 2023TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPTcited by 59
- 2024Interpretable LLM-based Table Question Answeringcited by 18
- 2023Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Datacited by 11
- 2024Tabular Transfer Learning via Prompting LLMscited by 10
- 2025Hybrid Graphs for Table-and-Text based Question Answering using LLMscited by 7
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