KRAG Framework for Enhancing LLMs in the Legal Domain

TL;DR

KRAG is a new framework that enhances large language models' ability to handle complex legal texts by incorporating critical domain knowledge and structured reasoning, significantly improving their performance in legal applications.

Contribution

The paper introduces KRAG, a novel knowledge representation framework, and implements Soft PROLEG to improve LLMs' legal reasoning and explanation capabilities.

Findings

KRAG improves LLM performance on legal tasks.

Soft PROLEG enables structured legal reasoning.

Enhanced understanding of legal terminology and relationships.

Abstract

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.