ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification
For researchers in tabular QA, this provides a first benchmark and framework for handling underspecified queries via dialogue, addressing a known gap in LLM-based systems.
The paper introduces ODUTQA-MDC, a task and benchmark for open-domain tabular QA with underspecified queries, featuring 209 tables and 25,105 QA pairs. The proposed MAIC-TQA framework effectively detects ambiguities and clarifies them through multi-turn dialogue, establishing a new resource for this area.
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.