PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915758480556032 |
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| author | Shi, Yuzhen Liu, Huanghai Hu, Yiran Song, Gaojie Xu, Xinran Ma, Yubo Tang, Tianyi Zhang, Li Chen, Qingjing Feng, Di Lv, Wenbo Wu, Weiheng Yang, Kexin Yang, Sen Wang, Wei Shi, Rongyao Qiu, Yuanyang Qi, Yuemeng Zhang, Jingwen Sui, Xiaoyu Chen, Yifan Zhang, Yi Yang, An Yu, Bowen Liu, Dayiheng Lin, Junyang Shen, Weixing Zhao, Bing Clarke, Charles L. A. Wei, Hu |
| author_facet | Shi, Yuzhen Liu, Huanghai Hu, Yiran Song, Gaojie Xu, Xinran Ma, Yubo Tang, Tianyi Zhang, Li Chen, Qingjing Feng, Di Lv, Wenbo Wu, Weiheng Yang, Kexin Yang, Sen Wang, Wei Shi, Rongyao Qiu, Yuanyang Qi, Yuemeng Zhang, Jingwen Sui, Xiaoyu Chen, Yifan Zhang, Yi Yang, An Yu, Bowen Liu, Dayiheng Lin, Junyang Shen, Weixing Zhao, Bing Clarke, Charles L. A. Wei, Hu |
| contents | 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://github.com/skylenage/PLawbench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16669 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice Shi, Yuzhen Liu, Huanghai Hu, Yiran Song, Gaojie Xu, Xinran Ma, Yubo Tang, Tianyi Zhang, Li Chen, Qingjing Feng, Di Lv, Wenbo Wu, Weiheng Yang, Kexin Yang, Sen Wang, Wei Shi, Rongyao Qiu, Yuanyang Qi, Yuemeng Zhang, Jingwen Sui, Xiaoyu Chen, Yifan Zhang, Yi Yang, An Yu, Bowen Liu, Dayiheng Lin, Junyang Shen, Weixing Zhao, Bing Clarke, Charles L. A. Wei, Hu Computation and Language Artificial Intelligence Computers and Society 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://github.com/skylenage/PLawbench. |
| title | PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2601.16669 |