ReasonTabQA: A Comprehensive Benchmark for Table Question Answering from Real World Industrial Scenarios
Changzai Pan, Jie Zhang, Kaiwen Wei, Chenshuo Pan, Yu Zhao, Jingwang Huang, Jian Yang, Zhenhe Wu, Haoyang Zeng, Xiaoyan Gu, Weichao Sun, Yanbo Zhai, Yujie Mao, Zhuoru Jiang, Jiang Zhong, Shuangyong Song, Yongxiang Li, Zhongjiang He

TL;DR
ReasonTabQA introduces a large-scale bilingual benchmark for industrial table question answering, emphasizing complex multi-table reasoning and explicit reasoning chains, to challenge and improve LLM capabilities in real-world scenarios.
Contribution
The paper presents ReasonTabQA, a comprehensive industrial TableQA benchmark with detailed annotations and reasoning chains, and proposes TabCodeRL, a reinforcement learning approach for logical reasoning.
Findings
TabCodeRL improves performance on open-source LLMs.
Persistent performance gap highlights industrial TableQA complexity.
ReasonTabQA covers 30 industry domains with 1,932 tables.
Abstract
Recent advancements in Large Language Models (LLMs) have significantly catalyzed table-based question answering (TableQA). However, existing TableQA benchmarks often overlook the intricacies of industrial scenarios, which are characterized by multi-table structures, nested headers, and massive scales. These environments demand robust table reasoning through deep structured inference, presenting a significant challenge that remains inadequately addressed by current methodologies. To bridge this gap, we present ReasonTabQA, a large-scale bilingual benchmark encompassing 1,932 tables across 30 industry domains such as energy and automotive. ReasonTabQA provides high-quality annotations for both final answers and explicit reasoning chains, supporting both thinking and no-thinking paradigms. Furthermore, we introduce TabCodeRL, a reinforcement learning method that leverages table-aware…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks · Multimodal Machine Learning Applications
