{"ID":2877612,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19813","arxiv_id":"2508.19813","title":"T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables","abstract":"Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as 4 types of industrial tables. Furthermore, we propose an evaluation criteria to fairly measure the quality of report generation. The experiments on 25 widely-used LLMs reveal that even state-of-the-art models like Deepseek-R1 only achieves performance with 62.71 overall score, indicating that LLMs still have room for improvement on T2R-bench.","short_abstract":"Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity an...","url_abs":"https://arxiv.org/abs/2508.19813","url_pdf":"https://arxiv.org/pdf/2508.19813v4","authors":"[\"Jie Zhang\",\"Changzai Pan\",\"Kaiwen Wei\",\"Sishi Xiong\",\"Yu Zhao\",\"Xiangyu Li\",\"Jiaxin Peng\",\"Xiaoyan Gu\",\"Jian Yang\",\"Wenhan Chang\",\"Zhenhe Wu\",\"Jiang Zhong\",\"Shuangyong Song\",\"Yongxiang Li\",\"Xuelong Li\"]","published":"2025-08-27T11:55:40Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}