用知识图谱增强大模型法律推理能力，让AI更懂专业术语和逻辑。
KRAG Framework for Enhancing LLMs in the Legal Domain
- 引入知识实体与关系图谱，补足法律领域缺失的关键信息
- 通过推理图结构提升法律问答的逻辑性与解释力
- 适合法律AI开发、司法辅助系统研究者使用
本文提出知识表示增强生成（KRAG）框架，旨在提升大语言模型在特定领域应用中的表现。该框架强调在标准数据集中通常缺失的关键知识实体与关系的引入，而这些内容是大模型无法自行习得的。针对法律领域，本文设计了基于KRAG的Soft PROLEG实现模型，利用推理图辅助大模型生成结构化法律推理、论证与解释，以回应用户查询。无论作为独立框架还是与检索增强生成（RAG）结合，KRAG均显著提升了语言模型处理复杂法律文本与术语的能力。论文详细阐述了KRAG的方法论、Soft PROLEG的实现方式及其在更广泛专业知识领域的潜在应用，凸显其在推动专业领域自然语言理解与处理方面的重要作用。
原文摘要 · Abstract (English)
This paper introduces Knowledge Representation Augmented Generation (KRAG), a novel framework designed to enhance the capabilities of Large Language Models (LLMs) within domain-specific applications. KRAG points to the strategic inclusion of critical knowledge entities and relationships that are typically absent in standard data sets and which LLMs do not inherently learn. In the context of legal applications, we present Soft PROLEG, an implementation model under KRAG, which uses inference graphs to aid LLMs in delivering structured legal reasoning, argumentation, and explanations tailored to user inquiries. The integration of KRAG, either as a standalone framework or in tandem with retrieval augmented generation (RAG), markedly improves the ability of language models to navigate and solve the intricate challenges posed by legal texts and terminologies. This paper details KRAG's methodology, its implementation through Soft PROLEG, and potential broader applications, underscoring its significant role in advancing natural language understanding and processing in specialized knowledge domains.
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