用检索增强生成技术提升大模型对法律条文的理解与起草能力
ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulation
- 结合微调与检索增强生成，让模型读懂并引用最新法律文本
- 显著提升政策制定者解读法规和起草新法的效率与准确性
- 适合法律科技、政策研究及AI辅助立法领域的从业者使用
本研究探索通过微调大语言模型（LLMs）来更好支持政策制定者理解、分析和制定法律规范。为使模型深入理解法律文本，我们构建了一个面向法律领域需求的监督数据集。同时，引入检索增强生成（RAG）方法，使LLM能够访问并融合外部最新法律知识。该方法结合微调与RAG增强，不仅可处理法律信息，更能主动辅助政策制定者解读法规、起草符合现实需求的新法规。实验结果表明，该方法显著提升了法律研究与法规制定的效率，为不断演进的法律领域提供了重要工具。
原文摘要 · Abstract (English)
In this study, we explore the fine-tuning of Large Language Models (LLMs) to better support policymakers in their crucial work of understanding, analyzing, and crafting legal regulations. To equip the model with a deep understanding of legal texts, we curated a supervised dataset tailored to the specific needs of the legal domain. Additionally, we integrated the Retrieval-Augmented Generation (RAG) method, enabling the LLM to access and incorporate up-to-date legal knowledge from external sources. This combination of fine-tuning and RAG-based augmentation results in a tool that not only processes legal information but actively assists policymakers in interpreting regulations and drafting new ones that align with current needs. The results demonstrate that this approach can significantly enhance the effectiveness of legal research and regulation development, offering a valuable resource in the ever-evolving field of law.
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