RoSiLC-RS:A Robust Similar Legal Case Recommender System Empowered by Large Language Model and Step-Back Prompting

Abstract

Legal case recommendation systems face significant challenges in the era of Large Language Models (LLMs). While LLMs offer unprecedented opportunities for understanding legal texts, they also introduce risks through AI-generated false legal content. Our survey reveals concerning gaps in public awareness: 41% of respondents incorrectly believe AI never generates false legal information, while only 6% understand potential legal liabilities. To address these issues, we propose RoSiLC-RS, a Robust Similar Legal Case Recommender System that guides LLMs to understand legal concepts at a higher abstraction level. Our system employs four key components: (1) abstraction processing to extract core legal elements, (2) semantic matching to identify similar case features, (3) LLM-powered explanation generation to provide detailed recommendation rationales, enhancing system explainability, and (4) a specialized detection module to identify and filter AI-generated false content. Comprehensive experiments on real-world legal datasets demonstrate that our method significantly outperforms traditional retrieval approaches in precision, relevance, explainability, and resistance to AI-generated content interference. This research provides both technological solutions and insights for the safe application of LLMs in legal domains.