ODUTQA-MDC: A Task for Open-Domain Underspecified Tabular QA with Multi-turn Dialogue-based Clarification
Year: 2026
Type: conference-paper
Abstract: The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with opendomain 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.The data and code are available at https://github.com/jensenw1/ ODUTQA-MDC.Q3: What is the greenery rate of the Vanke Light of the Future community?Q1: What is the greenery rate of the Vanke Light of the Future community in Bao'an District, Shenzhen?Q2: How is the environment of the Vanke Light of the Future community in Bao'an District, Shenzhen?Q4: What is the greenery rate of the Vanke community in Bao'an District, Shenzhen?
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G8160964939
OpenAlex ID: W7166840324