TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
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Computer Science > Computation and Language
arXiv:2506.18421 (cs)
[Submitted on 23 Jun 2025 (v1), last revised 21 Jul 2026 (this version, v3)]
Title:TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
Authors:Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng
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Abstract:The majority of data in businesses and industries is stored in tables, databases, and data warehouses. Reasoning with table-structured data poses significant challenges for large language models (LLMs) due to its hidden semantics, inherent complexity, and structured nature. One of these challenges is lacking an effective evaluation benchmark fairly reflecting the performances of LLMs on broad table reasoning abilities. In this paper, we fill in this gap by presenting a comprehensive table reasoning benchmark, TReB. Firstly, we propose a taxonomy to systematically measure both shallow table understanding abilities and deep table reasoning abilities, covering a total of 26 sub-tasks. We then construct a high quality dataset through a dedicated data processing and synthesis procedure. Based on these well-constructed samples, we design an evaluation framework to robustly measure table reasoning capabilities with three distinct inference modes. Experimental results with our data and framework reveal that existing LLMs still have significant room for improvement in addressing the complex and real world table related tasks. Both the dataset and evaluation framework are publicly available, with the dataset hosted on this https URL, and the framework on this https URL.
| Comments: | published by SIGIR 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2506.18421 [cs.CL] |
| (or arXiv:2506.18421v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.18421
arXiv-issued DOI via DataCite
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| Journal reference: | Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2026, 3267-3275 |
| Related DOI: | https://doi.org/10.1145/3805712.3808617
DOI(s) linking to related resources
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Submission history
From: Ce Chi [view email][v1] Mon, 23 Jun 2025 09:02:04 UTC (572 KB)
[v2] Mon, 14 Jul 2025 06:09:12 UTC (572 KB)
[v3] Tue, 21 Jul 2026 03:32:24 UTC (457 KB)
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