@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://preview.aclanthology.org/ingest-acl/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/}."
}