@inproceedings{zhang-etal-2025-t2r,
    title = "{T}2{R}-{BENCH}: A Benchmark for Real World Table-to-Report Task",
    author = "Zhang, Jie  and
      Pan, Changzai  and
      Xiong, Sishi  and
      Wei, Kaiwen  and
      Zhao, Yu  and
      Li, Xiangyu  and
      Peng, Jiaxin  and
      Gu, Xiaoyan  and
      Yang, Jian  and
      Chang, Wenhan  and
      Wu, Zhenhe  and
      Zhong, Jiang  and
      Song, Shuangyong  and
      Li, Xuelong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1141/",
    pages = "22438--22462",
    ISBN = "979-8-89176-332-6",
    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 \textbf{table-to-report} task and construct a bilingual benchmark named \textbf{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 four types of industrial tables. Furthermore, we propose a novel evaluation criteria to fairly measure the quality of report generation. Expeimental results show that Deepseek-R1 only achieves the best performance with 62.71{\%} overall score, indicating that LLMs still have room for improvement on T2R-bench."
}