AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL

• URL: http://arxiv.org/abs/2508.15276v1
• Date: Thu, 21 Aug 2025 06:10:28 GMT
• Title: AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
• Authors: Zhongjun Ding, Yin Lin, Tianjing Zeng,
• Abstract summary: We demonstrate Ambi, an interactive system that automatically detects query ambiguities and guides users through multiple-choice questions to clarify their intent.<n>Ambi achieves 87.2% in ambiguity detection and improves exact accuracy by 50% when integrated with Text-to- dataset systems.
• Score: 0.9217021281095907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-SQL systems translate natural language questions into SQL queries, providing substantial value for non-expert users. While large language models (LLMs) show promising results for this task, they remain error-prone. Query ambiguity has been recognized as a major obstacle for LLM-based Text-to-SQL systems, leading to misinterpretation of user intent and inaccurate SQL generation. We demonstrate AmbiSQL, an interactive system that automatically detects query ambiguities and guides users through intuitive multiple-choice questions to clarify their intent. Our approach introduces a fine-grained ambiguity taxonomy for identifying ambiguities that affect database element mapping and LLM reasoning, then incorporates user feedback to rewrite ambiguous questions. Evaluation on an ambiguous query dataset shows that AmbiSQL achieves 87.2% precision in ambiguity detection and improves SQL exact match accuracy by 50% when integrated with Text-to-SQL systems. Our demonstration showcases the significant performance gains and highlights the system's practical usability. Code repo and demonstration are available at: https://github.com/JustinzjDing/AmbiSQL.

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