增强法律领域LLMs的KRAG框架
KRAG Framework for Enhancing LLMs in the Legal Domain
AI总结：
本文提出知识表示增强生成（KRAG）框架及其法律实现模型Soft PROLEG，通过推理图辅助LLMs提供结构化法律推理，显著提升模型在法律领域的理解与处理能力。
摘要：
本文介绍了知识表示增强生成（KRAG），这是一种旨在增强大型语言模型（LLMs）在特定领域应用中能力的新型框架。KRAG指向战略性包含关键知识实体和关系，这些实体和关系通常在标准数据集中缺失，且LLMs不会内在学习。在法律应用的背景下，我们提出了Soft PROLEG，这是KRAG下的一个实现模型，它使用推理图来辅助LLMs提供针对用户查询量身定制的结构化法律推理、论证和解释。KRAG的整合，无论是作为独立框架还是与检索增强生成（RAG）协同，都显著提高了语言模型应对和解决法律文本及术语所提出的复杂挑战的能力。本文详细介绍了KRAG的方法论、通过Soft PROLEG的实现以及潜在的更广泛应用，强调了其在推进专业知识领域自然语言理解和处理方面的重要作用。
英文摘要：
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.
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。
