AILQA：评估适用于印度法律体系的人工智能驱动的法律问答系统
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
浏览论文内容
中文总结 AI 辅助
该研究针对印度法律体系开发AILQA系统，利用多种模型应对挑战，通过严格评估强调RAG范式提升答案质量，评估其在标准化测试中的表现，讨论挑战并给出未来研究方向，为增强法律决策支持系统助力。
中文摘要 AI 辅助
这项全面研究介绍了一种专门针对印度法律背景的先进人工智能法律问答（AILQA）系统。AILQA利用各种嵌入和生成模型，包括最近的大语言模型（LLMs），以应对印度法律文本复杂多样性质带来的独特挑战，并提高对法律问题回答的准确性和可靠性。我们使用词汇和语义指标并结合专家法律反馈进行了严格评估，以确保相关性和准确性。研究结果强调了检索增强生成（RAG）范式在提高答案质量方面的有效性，特别是在复杂法律领域。此外，我们评估了在诸如全印度律师考试（AIBE）等标准化测试中的表现，为实际应用提供了有力基准。在研究的评估协议下，一些人工智能生成的回答获得了比现有参考答案更高的评分，特别是当它们包含准确且相关的支持细节时。我们还讨论了遇到的挑战，如对精确上下文的需求和模型幻觉的风险，并提出了未来研究方向以进一步完善法律领域的人工智能能力。这项研究旨在为增强法律决策支持系统铺平道路，使其对法律专业人员和公众都更易获取且有效。
英文摘要
This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs), to address the unique challenges posed by the intricate and diverse nature of Indian legal texts and to enhance the accuracy and reliability of responses to legal questions. We conducted rigorous evaluations using both lexical and semantic metrics, enriched by expert legal feedback, to ensure relevance and accuracy. Our findings underscore the effectiveness of the Retrieval-Augmented Generation (RAG) paradigm in improving answer quality, particularly in complex legal domains. Additionally, we assessed performance on standardized tests such as the All India Bar Examination (AIBE), thereby providing a robust benchmark for practical applications. Under the study's evaluation protocol, some AI-generated responses received higher ratings than the available reference answers, particularly when they contained accurate and relevant supporting details. This finding is specific to the evaluated dataset and rating criteria and should not be interpreted as evidence that the models generally outperform qualified legal professionals. We also discuss the challenges encountered, such as the need for precise context and the risks of model hallucination, and propose directions for future research to further refine AI capabilities in the legal field. This study aims to pave the way for enhanced legal decision-support systems, making them more accessible and effective for legal professionals and the public alike.
发表机构
- Indian Institute of Technology（印度理工学院）
- Symbiosis Law School（共生法学院）
- Indian Institute of Science Education and Research（印度科学教育与研究学院）
- University of Birmingham（伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。
