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?

The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering.