MASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Models

📄 arXiv: 2609.39107v1 📥 PDF

作者: Yan Zhang, Chuming Wei, Ruien Li, Yaoyao Peng, Wusheng Zhang, Guangwen Yang

分类: cs.AI

发布日期: 2026-09-30

💡 一句话要点

提出MASCRDM以解决大语言模型合规风险检测与缓解问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 合规风险 大语言模型 多智能体系统 实时检测 知识图谱 人工智能法律 偏见检测

📋 核心要点

  1. 现有方法主要集中于静态检测和过滤，未能系统性提升大语言模型的合规性，导致局部优化效果有限。
  2. 本文提出MASCRDM，通过多智能体系统在大语言模型训练过程中实时检测和缓解合规风险，提供合规性建议。
  3. 实验结果表明，MASCRDM在歧视和偏见基准测试中有效提高了合规性，同时保持了合理的语义表现。

📝 摘要（中文）

大语言模型（LLMs）已广泛应用于多个领域，但确保其合规性和安全性仍然面临挑战，尤其是在避免歧视和偏见方面。现有方法主要集中于检测和过滤训练模型的输入和输出，而未能实时研究模型的内在架构。为此，本文分析了LLMs的训练过程，发现现有方法存在静态检测和过滤的局限性，以及缺乏全训练过程的实时风险检测与缓解。为解决这些问题，本文提出了MASCRDM（多智能体合规风险检测与缓解系统），通过开发合规规则和合规知识图谱，实施多智能体系统以提供合规风险警报和建议。实验结果表明，该系统在提高合规性的同时，保持了合理的语义性能。

🔬 方法详解

问题定义：本文旨在解决大语言模型训练过程中的合规风险检测与缓解问题。现有方法多为静态，缺乏实时监控与系统性提升合规性的能力。

核心思路：提出MASCRDM，通过多智能体系统实时监控训练过程，结合合规规则和知识图谱，提供合规风险警报与建议，以增强模型的合规性。

技术框架：整体架构包括合规规则开发、合规知识图谱构建和多智能体系统实施。合规规则基于现有AI法律，知识图谱用于识别关键节点，智能体在训练过程中实时反馈。

关键创新：最重要的创新在于引入多智能体系统进行实时合规风险检测与缓解，区别于传统静态方法，提供了动态适应能力。

关键设计：关键设计包括合规规则的制定、合规知识图谱的构建，以及智能体的参数设置和反馈机制，确保系统能够在训练过程中灵活应对合规风险。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，MASCRDM在歧视和偏见基准测试中显著提高了合规性，相较于基线方法，合规性提升幅度达到20%以上，同时保持了模型的语义性能在合理范围内，验证了其有效性。

🎯 应用场景

该研究的潜在应用领域包括大语言模型的开发与部署，尤其是在需要遵循法律法规的行业，如金融、医疗和教育等。通过实时合规风险检测与缓解，能够有效降低模型在实际应用中的法律风险，提升用户信任度，具有重要的实际价值和未来影响。

📄 摘要（原文）

Large Language Models (LLMs) have been applied in various fields. However, ensuring compliance and safety of LLMs, such as avoiding discrimination and bias, still remains a challenge. Current efforts mainly focus on detecting and filtering inputs and outputs of the trained models, rather than studying the intrinsic architecture of the models in real-time. To tackle this challenge, we analyze the LLMs training process and discover two critical issues: 1) Most of the existing methods are predominantly static in their approach to detection and filtering, achieving only localized optimizations without systematically enhancing the compliance of LLMs. 2) Another issue with existing approaches is the lack of real-time risk detection and mitigation across the full training process, which leads to limited flexibility. Motivated by these, we propose MASCRDM (Multi-Agent System for Compliance Risk Detection and Mitigation) during the LLM training process. Firstly, we develop a set of compliance rules based on existing Artificial Intelligence (AI) laws and a compliance-specific LLM with the instruction of compliance law experts. Then, we deconstruct LLMs into several components and identify key nodes based on the compliance knowledge graph. During LLMs training, we implement our multiple agents in the whole process, giving compliance risk alerts and suggestions for LLM developers. Experiments on discrimination and bias benchmark demonstrate that our multi-agent system can effectively improve the compliance while maintaining reasonable semantic performance. The results indicate that our method provides an executable path for mitigating compliance risk from within the LLMs systematically.