用多智能体系统解决多人使用大模型时的偏好冲突问题
MAP: Multi-user Personalization with Collaborative LLM-powered Agents
- 设计三阶段用户中心流程：反思-分析-反馈
- 12人测试显示系统有效缓解冲突并提升可用性
- 适合需要协同个性化服务的团队或平台
大型语言模型及其智能体在多用户场景中的广泛应用，凸显了应对多样化偏好和冲突指令的需求。基于冲突解决理论，我们提出一种以用户为中心的多用户个性化工作流，包含反思、分析和反馈三个阶段，并构建了MAP——一个用于多用户个性化的多智能体系统。通过将子任务分配给专业智能体，MAP（1）检索并反思相关用户信息，借助智能体间交互提升可靠性；（2）提供详细分析以增强透明度与可用性；（3）集成用户反馈，实现结果的迭代优化。12名用户的实证研究结果表明，MAP在冲突解决方面表现有效且易用，同时强调了用户参与验证与故障管理的重要性。本工作展示了多智能体系统在实现用户中心化多用户个性化流程中的潜力，并为多用户个性化提供了实践启示。
原文摘要 · Abstract (English)
The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP -- a \textbf{M}ulti-\textbf{A}gent system for multi-user \textbf{P}ersonalization -- to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-to-agent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (n=12) highlight MAP's effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts.
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