T2R-bench: A Benchmark for Generating Article-Level Reports from Real
  World Industrial Tables
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.
68b1484bb19c540001484a18
Jie Zhang
68b1484bb19c540001484a19
Changzai Pan
68b1484bb19c540001484a1a
Kaiwen Wei
68b1484bb19c540001484a1b
Sishi Xiong
68b1484bb19c540001484a1c
Yu Zhao
68b1484bb19c540001484a1d
Xiangyu Li
68b1484bb19c540001484a1e
Jiaxin Peng
68b1484bb19c540001484a1f
Xiaoyan Gu
68b1484bb19c540001484a20
Jian Yang
68b1484bb19c540001484a21
Wenhan Chang
claimed_verified
2025-09-03T08:29:44.593Z
6401f73782bbdfe4b7c3492d
/avatars/fe3c581af3e1684349a132aa49893d90.svg
Wenhan Chang
user
changwh5
68b1484bb19c540001484a22
Zhenhe Wu
68b1484bb19c540001484a23
Jiang Zhong
68b1484bb19c540001484a24
Shuangyong Song
68b1484bb19c540001484a25
Yongxiang Li
68b1484bb19c540001484a26
Xuelong Li
T2R-bench: A Benchmark for Generating Article-Level Reports from Real
  World Industrial Tables
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.
A bilingual benchmark named T2R-bench is proposed to evaluate the performance of large language models in generating reports from tables, highlighting the need for improvement in this task.
large language models table reasoning table-to-report task T2R-bench industrial tables report generation evaluation criteria Deepseek-R1