An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis
This addresses challenges in intelligent legal assistance systems by enhancing LLMs' ability to understand complex legal concepts and maintain reasoning consistency, though it appears incremental as it builds on existing prompt engineering and knowledge graph techniques.
The researchers tackled the problem of improving LLMs' legal dispute analysis by developing a framework that integrates prompt engineering with multidimensional knowledge graphs, resulting in sensitivity increases of 11.1%-11.3%, specificity by 5.4%-6.0%, and citation accuracy by 29.5%-39.7%.
Legal dispute analysis is crucial for intelligent legal assistance systems. However, current LLMs face significant challenges in understanding complex legal concepts, maintaining reasoning consistency, and accurately citing legal sources. This research presents a framework combining prompt engineering with multidimensional knowledge graphs to improve LLMs' legal dispute analysis. Specifically, the framework includes a three-stage hierarchical prompt structure (task definition, knowledge background, reasoning guidance) along with a three-layer knowledge graph (legal ontology, representation, instance layers). Additionally, four supporting methods enable precise legal concept retrieval: direct code matching, semantic vector similarity, ontology path reasoning, and lexical segmentation. Through extensive testing, results show major improvements: sensitivity increased by 11.1%-11.3%, specificity by 5.4%-6.0%, and citation accuracy by 29.5%-39.7%. As a result, the framework provides better legal analysis and understanding of judicial logic, thus offering a new technical method for intelligent legal assistance systems.