T2R-bench: A Benchmark for Generating Article-Level Reports from Real
  World Industrial Tables
Authors: name: Jie Zhang; name: Changzai Pan; name: Kaiwen Wei; name: Sishi Xiong; name: Yu Zhao; name: Xiangyu Li; name: Jiaxin Peng; name: Xiaoyan Gu; name: Jian Yang; name: Wenhan Chang; name: Zhenhe Wu; name: Jiang Zhong; name: Shuangyong Song; name: Yongxiang Li; name: Xuelong Li
Published: 2025-08-27 11:55:40

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. Source code and data will be available after acceptance.