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.

本研究解决法律领域中人工智能（AI）的可靠性问题，特别是AI模型产生不准确或误导性信息的“幻觉”现象。提出了一种将混合专家系统与知识基础架构结合的新框架，利用专业模块和先进技术（如检索增强生成（RAG）和知识图谱（KG））来提高法律服务的精准性与上下文相关性，从而展示在法律任务中的显著性能提升和可扩展性。

可靠法律人工智能的综合框架：结合专业专家系统与自适应细化