解决开放域表格问答中模糊问题的多轮对话澄清难题
ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification
- 构建多轮对话澄清机制，主动识别并澄清模糊问题
- 发布包含25,105组问答的大规模基准数据集
- 适合研究对话式问答与表格推理的学者使用
大语言模型虽提升了表格问答能力，但在开放域中面对表达不明确或不确定的问题时仍表现不佳。为此，我们提出ODUTQA-MDC任务及首个综合性基准，包含：(1) 由209张表格组成的大型数据集，含25,105组问答对；(2) 细粒度标注方案以支持精准评估；(3) 动态澄清接口，模拟用户反馈实现交互式测评。同时提出MAIC-TQA多智能体框架，能有效检测模糊性、通过对话澄清并优化答案。实验验证了该基准与框架的有效性，确立其为推进对话式、关注模糊性的表格问答研究的关键资源。
原文摘要 · Abstract (English)
The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with open-domain queries exhibiting underspecified or uncertain expressions. To address this, we introduce the ODUTQA-MDC task and the first comprehensive benchmark to tackle it. This benchmark includes: (1) a large-scale ODUTQA dataset with 209 tables and 25,105 QA pairs; (2) a fine-grained labeling scheme for detailed evaluation; and (3) a dynamic clarification interface that simulates user feedback for interactive assessment. We also propose MAIC-TQA, a multi-agent framework that excels at detecting ambiguities, clarifying them through dialogue, and refining answers. Experiments validate our benchmark and framework, establishing them as a key resource for advancing conversational, underspecification-aware Tabular QA research.
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