一种面向加尔各答高等法院判决的数据科学方法：基于高效LLM与RAG的摘要生成与相似案件检索框架
A Data Science Approach to Calcutta High Court Judgments: An Efficient LLM and RAG-powered Framework for Summarization and Similar Cases Retrieval
AI总结：
本文提出一种结合微调Pegasus模型与RAG技术的框架，用于加尔各答高等法院判决的摘要生成和相似案件检索，以提升法律研究效率。
摘要：
司法机构作为民主制度的三大支柱之一，正面临日益增多的法律问题，需要审慎使用司法资源。本研究提出了一个综合框架，利用数据科学方法，特别是大语言模型（LLM）和检索增强生成（RAG）技术，以提高加尔各答高等法院判决分析的效率。该框架聚焦于两个关键方面：第一，构建一个稳健的摘要生成机制，将复杂的法律文本提炼为简洁连贯的摘要；第二，开发一个用于检索相似案件的智能系统，以协助法律专业人员进行研究和决策。通过使用案件批注摘要对Pegasus模型进行微调，我们在法律案件摘要生成方面取得了显著改进。我们的两步摘要技术保留了关键的法律语境，从而能够为RAG构建一个全面的向量数据库。基于RAG的框架能够高效地根据用户查询检索相似案件，提供全面的概览和摘要。该技术不仅提高了法律研究效率，还帮助法律专业人员和学生更便捷地获取和掌握关键法律信息，从而惠及整体法律环境。
英文摘要：
The judiciary, as one of democracy's three pillars, is dealing with a rising amount of legal issues, needing careful use of judicial resources. This research presents a complex framework that leverages Data Science methodologies, notably Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) techniques, to improve the efficiency of analyzing Calcutta High Court verdicts. Our framework focuses on two key aspects: first, the creation of a robust summarization mechanism that distills complex legal texts into concise and coherent summaries; and second, the development of an intelligent system for retrieving similar cases, which will assist legal professionals in research and decision making. By fine-tuning the Pegasus model using case head note summaries, we achieve significant improvements in the summarization of legal cases. Our two-step summarizing technique preserves crucial legal contexts, allowing for the production of a comprehensive vector database for RAG. The RAG-powered framework efficiently retrieves similar cases in response to user queries, offering thorough overviews and summaries. This technique not only improves legal research efficiency, but it also helps legal professionals and students easily acquire and grasp key legal information, benefiting the overall legal scenario.
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。
