AILS-NTUA at SemEval-2025 Task 8: Language-to-Code prompting and Error Fixing for Tabular Question Answering
This addresses the challenge of improving accuracy and interpretability in tabular question answering for users of large language models, though it appears incremental as it builds on existing prompting techniques.
The paper tackled the problem of answering natural language questions over tabular data by developing a system that uses LLM prompting to translate queries into executable code, achieving first place in both subtasks of the SemEval-2025 competition and significantly outperforming the baseline.
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.