@inproceedings{shi-etal-2026-plawbench,
title = "{PLAWBENCH}: A Rubric-Based Benchmark for Evaluating {LLM}s in Real-World Legal Practice",
author = "Shi, Yuzhen and
Liu, Huanghai and
HU, Yiran and
Gaojie, Song and
Xinran, Xu and
Ma, Yubo and
Tang, Tianyi and
Zhang, Li and
Chen, Qingjing and
Di, Feng and
Lv, Wenbo and
Wu, Weiheng and
Yang, Kexin and
Yang, Sen and
Wang, Wei and
Shi, Rongyao and
Yuanyang, Qiu and
Qi, Yuemeng and
Jingwen, Zhang and
Xiaoyu, Sui and
Chen, Yifan and
Yi, Zhang and
Yang, An and
Yu, Bowen and
Liu, Dayiheng and
Lin, Junyang and
Shen, Weixing and
Zhao, Bing and
Clarke, Charles L. A. and
Wei, HU",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.458/",
pages = "10067--10116",
ISBN = "979-8-89176-390-6",
abstract = "As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real-world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model{'}s ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine-grained assessment. Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine-grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: \url{https://anonymous.4open.science/r/PLawbench-B524/}."
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<abstract>As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real-world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model’s ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine-grained assessment. Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine-grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: https://anonymous.4open.science/r/PLawbench-B524/.</abstract>
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%0 Conference Proceedings
%T PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
%A Shi, Yuzhen
%A Liu, Huanghai
%A HU, Yiran
%A Gaojie, Song
%A Xinran, Xu
%A Ma, Yubo
%A Tang, Tianyi
%A Zhang, Li
%A Chen, Qingjing
%A Di, Feng
%A Lv, Wenbo
%A Wu, Weiheng
%A Yang, Kexin
%A Yang, Sen
%A Wang, Wei
%A Shi, Rongyao
%A Yuanyang, Qiu
%A Qi, Yuemeng
%A Jingwen, Zhang
%A Xiaoyu, Sui
%A Chen, Yifan
%A Yi, Zhang
%A Yang, An
%A Yu, Bowen
%A Liu, Dayiheng
%A Lin, Junyang
%A Shen, Weixing
%A Zhao, Bing
%A Clarke, Charles L. A.
%A Wei, H. U.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F shi-etal-2026-plawbench
%X As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real-world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model’s ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine-grained assessment. Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine-grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: https://anonymous.4open.science/r/PLawbench-B524/.
%U https://aclanthology.org/2026.acl-long.458/
%P 10067-10116
Markdown (Informal)
[PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice](https://aclanthology.org/2026.acl-long.458/) (Shi et al., ACL 2026)
ACL
- Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, and HU Wei. 2026. PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10067–10116, San Diego, California, United States. Association for Computational Linguistics.