用专家系统+AI技术解决法律AI幻觉问题，提升可靠性。
A Comprehensive Framework for Reliable Legal AI: Combining Specialized Expert Systems and Adaptive Refinement
- 融合专业模块与知识架构，分领域处理法律任务。
- 结合RAG、知识图谱和人类反馈强化学习，减少错误生成。
- 适合法律科技公司与合规部门，提升服务准确率。
本文探讨人工智能在法律领域的演进角色，聚焦其在文件审查、法律研究和合同起草中的应用潜力。然而，当前仍面临模型生成虚假或误导性信息（即‘幻觉’）的问题，严重影响法律场景下的可信度。为此，文章提出一种新框架，结合专用专家系统与基于知识的架构，以提升AI法律服务的精准度与上下文相关性。该框架采用专注于特定法律领域的专业化模块，并融入结构化操作指南增强决策能力。同时，利用检索增强生成（RAG）、知识图谱（KG）及基于人类反馈的强化学习（RLHF）等先进AI技术，显著提升系统准确性。实验表明，该方法在多项法律任务中表现优于现有模型，为提供更可及、更经济的法律服务提供了可扩展解决方案。文章还阐述了方法论、系统架构及未来研究方向。
原文摘要 · Abstract (English)
This article discusses the evolving role of artificial intelligence (AI) in the legal profession, focusing on its potential to streamline tasks such as document review, research, and contract drafting. However, challenges persist, particularly the occurrence of "hallucinations" in AI models, where they generate inaccurate or misleading information, undermining their reliability in legal contexts. To address this, the article proposes a novel framework combining a mixture of expert systems with a knowledge-based architecture to improve the precision and contextual relevance of AI-driven legal services. This framework utilizes specialized modules, each focusing on specific legal areas, and incorporates structured operational guidelines to enhance decision-making. Additionally, it leverages advanced AI techniques like Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), and Reinforcement Learning from Human Feedback (RLHF) to improve the system's accuracy. The proposed approach demonstrates significant improvements over existing AI models, showcasing enhanced performance in legal tasks and offering a scalable solution to provide more accessible and affordable legal services. The article also outlines the methodology, system architecture, and promising directions for future research in AI applications for the legal sector.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。