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.

翻译：本文探讨了人工智能（AI）在法律职业中不断演变的角色，重点关注其在简化文件审阅、法律研究及合同起草等任务方面的潜力。然而，挑战依然存在，尤其是AI模型中出现的“幻觉”现象——即模型生成不准确或误导性信息，这削弱了其在法律场景中的可靠性。为解决这一问题，本文提出了一种新颖框架，该框架结合了混合专家系统与基于知识的架构，以提高AI驱动法律服务的精确性与情境相关性。该框架利用多个专业化模块，每个模块专注于特定法律领域，并通过整合结构化操作指南来增强决策能力。此外，框架还运用了检索增强生成（RAG）、知识图谱（KG）以及基于人类反馈的强化学习（RLHF）等先进AI技术，以提升系统准确性。所提出的方法相较于现有AI模型展现出显著改进，在法律任务中表现出更优的性能，并为提供更易获取、更经济的法律服务提供了可扩展的解决方案。本文还概述了法律领域AI应用的方法论、系统架构以及未来研究的前景方向。