Hong Kong case law translation presents significant challenges: manual methods suffer from high costs and inconsistent quality, while both traditional machine translation and approaches relying solely on Large Language Models (LLMs) often fail to ensure legal terminology accuracy, culturally embedded nuances, and strict linguistic structures. To overcome these limitations, this study proposes TransLaw, a multi-agent framework that decomposes translation into word-level expression, sentence-level translation, and multidimensional review, integrating a specialized Hong Kong legal glossary database, Retrieval-Augmented Generation (RAG), and iterative feedback. Experiments on our newly constructed HKCFA Judgment 97-22 dataset, benchmarking 13 open-source and commercial LLMs, demonstrate that TransLaw significantly outperforms single-agent baselines across all evaluated models. Human evaluation confirms the framework's effectiveness in terms of legal semantic accuracy, structural coherence, and stylistic fidelity, while noting that it still trails human experts in contextualizing complex terminology and stylistic naturalness.

翻译：香港判例法翻译面临重大挑战：人工方法成本高昂且质量参差不齐，而传统机器翻译以及单纯依赖大语言模型（LLMs）的方法往往难以确保法律术语的准确性、文化内嵌的细微差别以及严格的语言结构。为克服这些局限，本研究提出TransLaw，一个将翻译分解为词汇级表达、句子级翻译和多维审校的多智能体框架，该框架集成了专门的香港法律术语数据库、检索增强生成（RAG）以及迭代反馈机制。在我们新构建的HKCFA Judgment 97-22数据集上进行的实验，对13个开源和商业LLMs进行了基准测试，结果表明TransLaw在所有评估模型上均显著优于单智能体基线。人工评估证实了该框架在法律语义准确性、结构连贯性和文体忠实度方面的有效性，同时也指出其在复杂术语语境化和文体自然度方面仍落后于人类专家。