arXiv 2507.077482025-07-10cs.CLcs.AI

当大型语言模型遇上法律：双透镜分类法、技术进展与伦理治理

When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance

School of Law, China University of Political Science and Law（中国政法大学法学院）；Zhejiang University of Finance and Economics Dongfang College（浙江财经大学东洋学院）；School of Information Management for Law, China University of Political Science and Law（中国政法大学信息管理法学院）；Department of Artificial Intelligence, Chung-Ang University（成均馆大学人工智能系）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）

Peizhang Shao, Linrui Xu, Jinxi Wang, Wei Zhou, Xingyu Wu

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AI总结：

本文综述了LLMs在法律领域的应用，提出整合法律推理与专业本体的双透镜分类法，系统梳理了技术进展、核心挑战及伦理治理，为法律AI的未来发展奠定基础。

摘要：

本文建立了大型语言模型（LLMs）在法律领域应用的首篇全面综述。它首创了一种创新的双透镜分类法，整合了法律推理框架和专业知识本体，以系统统一历史研究与当代突破。基于Transformer的LLMs展现出上下文推理和生成性论证等涌现能力，通过动态捕捉法律语义和统一证据推理，克服了传统局限。在任务泛化、推理形式化、工作流整合方面记录了重大进展，并通过稀疏注意力机制和混合专家架构等技术创新，解决了文本处理、知识整合和评估严谨性方面的核心挑战。然而，LLMs的广泛采用引入了关键挑战：幻觉、可解释性不足、司法管辖区适应困难和伦理不对称。本综述提出了一种新颖的分类法，将法律角色映射到NLP子任务，并在计算上实现了Toulmin论证框架，从而系统化了推理、检索、预测和争议解决方面的进展。它确定了关键前沿领域，包括低资源系统、多模态证据整合和动态反驳处理。最终，这项工作为研究人员提供了技术路线图，为从业者在算法化未来中导航提供了概念框架，为下一代法律人工智能奠定了坚实基础。我们创建了一个GitHub仓库来索引相关论文：https://github.com/Kilimajaro/LLMs_Meet_Law。

英文摘要：

This paper establishes the first comprehensive review of Large Language Models (LLMs) applied within the legal domain. It pioneers an innovative dual lens taxonomy that integrates legal reasoning frameworks and professional ontologies to systematically unify historical research and contemporary breakthroughs. Transformer-based LLMs, which exhibit emergent capabilities such as contextual reasoning and generative argumentation, surmount traditional limitations by dynamically capturing legal semantics and unifying evidence reasoning. Significant progress is documented in task generalization, reasoning formalization, workflow integration, and addressing core challenges in text processing, knowledge integration, and evaluation rigor via technical innovations like sparse attention mechanisms and mixture-of-experts architectures. However, widespread adoption of LLM introduces critical challenges: hallucination, explainability deficits, jurisdictional adaptation difficulties, and ethical asymmetry. This review proposes a novel taxonomy that maps legal roles to NLP subtasks and computationally implements the Toulmin argumentation framework, thus systematizing advances in reasoning, retrieval, prediction, and dispute resolution. It identifies key frontiers including low-resource systems, multimodal evidence integration, and dynamic rebuttal handling. Ultimately, this work provides both a technical roadmap for researchers and a conceptual framework for practitioners navigating the algorithmic future, laying a robust foundation for the next era of legal artificial intelligence. We have created a GitHub repository to index the relevant papers: https://github.com/Kilimajaro/LLMs_Meet_Law.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。