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This paper explores the application of Retrieval-Augmented Generation (RAG) techniques with Large Language Models (LLMs) in the legal domain, focusing on enhancing model performance on standardized legal benchmarks like LexGLUE. We investigate the integration of RAG with the Mistral 7B model, a promising open-source LLM known for outperforming larger models in various tasks. Despite initial challenges, including suboptimal prompt design and context limitations, our study reveals insights into improving model accuracy and factuality, especially when explaining legal concepts. We conducted evaluations on three LexGLUE tasks: SCOTUS, ECtHR (Task A), and ECtHR (Task B), using the LangChain framework to implement RAG techniques. Although our results fell short of existing baselines, we identified key areas for improvement, such as optimizing chunk retrieval from external knowledge bases. Our findings underscore the potential of Mistral 7B and RAG techniques to advance legal reasoning tasks, with further refinement expected to yield competitive results in legal benchmarks.

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Leveraging RAG and Mistral 7B for Enhanced Performance on Legal Benchmarks

  • 会议论文

  • 发表日期：

    2025年

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  • Rajiv Kumar

摘要

This paper explores the application of Retrieval-Augmented Generation (RAG) techniques with Large Language Models (LLMs) in the legal domain, focusing on enhancing model performance on standardized legal benchmarks like LexGLUE. We investigate the integration of RAG with the Mistral 7B model, a promising open-source LLM known for outperforming larger models in various tasks. Despite initial challenges, including suboptimal prompt design and context limitations, our study reveals insights into improving model accuracy and factuality, especially when explaining legal concepts. We conducted evaluations on three LexGLUE tasks: SCOTUS, ECtHR (Task A), and ECtHR (Task B), using the LangChain framework to implement RAG techniques. Although our results fell short of existing baselines, we identified key areas for improvement, such as optimizing chunk retrieval from external knowledge bases. Our findings underscore the potential of Mistral 7B and RAG techniques to advance legal reasoning tasks, with further refinement expected to yield competitive results in legal benchmarks.