@inproceedings{wu-etal-2025-realhitbench,
title = "{R}eal{H}i{TB}ench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating {LLM}-Based Table Analysis",
author = "Wu, Pengzuo and
Yang, Yuhang and
Zhu, Guangcheng and
Ye, Chao and
Gu, Hong and
Lu, Xu and
Xiao, Ruixuan and
Bao, Bowen and
He, Yijing and
Zha, Liangyu and
Ye, Wentao and
Zhao, Junbo and
Wang, Haobo",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.371/",
doi = "10.18653/v1/2025.findings-acl.371",
pages = "7105--7137",
ISBN = "979-8-89176-256-5",
abstract = "With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we introduce **RealHiTBench**, a comprehensive benchmark designed to evaluate the performance of both LLMs and Multimodal LLMs (MLLMs) across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. RealHiTBench also includes a diverse collection of tables with intricate structures, spanning a wide range of task types. Our experimental results, using **25** state-of-the-art LLMs, demonstrate that RealHiTBench is indeed a challenging benchmark. Moreover, we also develop TreeThinker, a tree-based agent that organizes hierarchical headers into a tree structure for enhanced tabular reasoning, validating the importance of improving LLMs' perception of table hierarchies. We hope that our work will inspire further research on tabular data reasoning and the development of more robust models. The code and data are available at https://github.com/cspzyy/RealHiTBench."
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<abstract>With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we introduce **RealHiTBench**, a comprehensive benchmark designed to evaluate the performance of both LLMs and Multimodal LLMs (MLLMs) across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. RealHiTBench also includes a diverse collection of tables with intricate structures, spanning a wide range of task types. Our experimental results, using **25** state-of-the-art LLMs, demonstrate that RealHiTBench is indeed a challenging benchmark. Moreover, we also develop TreeThinker, a tree-based agent that organizes hierarchical headers into a tree structure for enhanced tabular reasoning, validating the importance of improving LLMs’ perception of table hierarchies. We hope that our work will inspire further research on tabular data reasoning and the development of more robust models. The code and data are available at https://github.com/cspzyy/RealHiTBench.</abstract>
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%0 Conference Proceedings
%T RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
%A Wu, Pengzuo
%A Yang, Yuhang
%A Zhu, Guangcheng
%A Ye, Chao
%A Gu, Hong
%A Lu, Xu
%A Xiao, Ruixuan
%A Bao, Bowen
%A He, Yijing
%A Zha, Liangyu
%A Ye, Wentao
%A Zhao, Junbo
%A Wang, Haobo
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wu-etal-2025-realhitbench
%X With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we introduce **RealHiTBench**, a comprehensive benchmark designed to evaluate the performance of both LLMs and Multimodal LLMs (MLLMs) across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. RealHiTBench also includes a diverse collection of tables with intricate structures, spanning a wide range of task types. Our experimental results, using **25** state-of-the-art LLMs, demonstrate that RealHiTBench is indeed a challenging benchmark. Moreover, we also develop TreeThinker, a tree-based agent that organizes hierarchical headers into a tree structure for enhanced tabular reasoning, validating the importance of improving LLMs’ perception of table hierarchies. We hope that our work will inspire further research on tabular data reasoning and the development of more robust models. The code and data are available at https://github.com/cspzyy/RealHiTBench.
%R 10.18653/v1/2025.findings-acl.371
%U https://aclanthology.org/2025.findings-acl.371/
%U https://doi.org/10.18653/v1/2025.findings-acl.371
%P 7105-7137
Markdown (Informal)
[RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis](https://aclanthology.org/2025.findings-acl.371/) (Wu et al., Findings 2025)
ACL
- Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, and Haobo Wang. 2025. RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7105–7137, Vienna, Austria. Association for Computational Linguistics.