系统梳理大模型在法律领域评估的挑战与方法
Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions
- 从真实法律实践出发，识别评估大模型的核心难题
- 分类整理现有评测方法与数据集，分析其优劣
- 为可信、合规的法律大模型评估提供未来方向
大型语言模型（LLMs）正被越来越多地应用于司法决策支持、法律实务辅助和面向公众的法律服务。尽管它们在处理法律知识与任务方面展现出强大潜力，但在真实法律场景中的部署仍引发诸多深层关切，不仅涉及表面准确性，更关乎法律推理过程的合理性以及公平性、可靠性等可信问题。因此，系统性评估大模型在法律任务中的表现，已成为其负责任应用的关键前提。本文基于真实法律实践，识别了评估法律领域大模型时面临的核心挑战，包括结果正确性、推理可靠性与可信度等问题。在此基础上，我们梳理并分类了现有评估方法与基准测试，涵盖任务设计、数据集与评价指标。进一步分析当前方法对上述挑战的覆盖程度，揭示其局限性，并提出未来研究方向：构建更贴近现实、更可靠且具备法律根基的评估框架。
原文摘要 · Abstract (English)
Large language models (LLMs) are being increasingly integrated into legal applications, including judicial decision support, legal practice assistance, and public-facing legal services. While LLMs show strong potential in handling legal knowledge and tasks, their deployment in real-world legal settings raises critical concerns beyond surface-level accuracy, involving the soundness of legal reasoning processes and trustworthy issues such as fairness and reliability. Systematic evaluation of LLM performance in legal tasks has therefore become essential for their responsible adoption. This survey identifies key challenges in evaluating LLMs for legal tasks grounded in real-world legal practice. We analyze the major difficulties involved in assessing LLM performance in the legal domain, including outcome correctness, reasoning reliability, and trustworthiness. Building on these challenges, we review and categorize existing evaluation methods and benchmarks according to their task design, datasets, and evaluation metrics. We further discuss the extent to which current approaches address these challenges, highlight their limitations, and outline future research directions toward more realistic, reliable, and legally grounded evaluation frameworks for LLMs in legal domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。