用多个AI助手协作解答巴西劳动法问题，更准更快。
HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation
- 设计多个专用AI代理协同工作，分工处理不同法律问题。
- 相比单个大模型，回答准确率和连贯性显著提升。
- 适合HR从业者快速合规审查，也适用于法律AI系统开发
巴西《劳动法典》（CLT）是规范劳工关系的核心法律框架，保障劳动者基本权益。然而其复杂性给人力资源（HR）专业人士带来合规难题。传统问答方式常导致效率低下、延迟和不一致。为此，本文提出一种基于大型语言模型（LLM）的多代理系统，通过专业化代理分工协作，并结合检索增强生成（RAG）提升上下文相关性。系统基于CrewAI实现，支持代理间协作与结果验证，有效减少错误信息。通过自动指标（如BLEU）、LLM评分及专家人工评估，对比单模型基线管道进行评估。结果显示，多代理方法在回答连贯性和正确性上均有提升，为HR提供更可靠、高效的法律咨询解决方案。本研究展示了多代理LLM架构在提升劳动法合规性与优化HR流程方面的潜力。
原文摘要 · Abstract (English)
The Consolidation of Labor Laws (CLT) serves as the primary legal framework governing labor relations in Brazil, ensuring essential protections for workers. However, its complexity creates challenges for Human Resources (HR) professionals in navigating regulations and ensuring compliance. Traditional methods for addressing labor law inquiries often lead to inefficiencies, delays, and inconsistencies. To enhance the accuracy and efficiency of legal question-answering (Q&A), a multi-agent system powered by Large Language Models (LLMs) is introduced. This approach employs specialized agents to address distinct aspects of employment law while integrating Retrieval-Augmented Generation (RAG) to enhance contextual relevance. Implemented using CrewAI, the system enables cooperative agent interactions, ensuring response validation and reducing misinformation. The effectiveness of this framework is evaluated through a comparison with a baseline RAG pipeline utilizing a single LLM, using automated metrics such as BLEU, LLM-as-judge evaluations, and expert human assessments. Results indicate that the multi-agent approach improves response coherence and correctness, providing a more reliable and efficient solution for HR professionals. This study contributes to AI-driven legal assistance by demonstrating the potential of multi-agent LLM architectures in improving labor law compliance and streamlining HR operations.
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