MAP: Multi-user Personalization with Collaborative LLM-powered Agents
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908274077466624 |
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| author | Lee, Christine Choi, Jihye Mutlu, Bilge |
| author_facet | Lee, Christine Choi, Jihye Mutlu, Bilge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MAP: Multi-user Personalization with Collaborative LLM-powered Agents Lee, Christine Choi, Jihye Mutlu, Bilge Human-Computer Interaction Artificial Intelligence Robotics I.2.7; I.2.9; I.2.1 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. |
| title | MAP: Multi-user Personalization with Collaborative LLM-powered Agents |
| topic | Human-Computer Interaction Artificial Intelligence Robotics I.2.7; I.2.9; I.2.1 |
| url | https://arxiv.org/abs/2503.12757 |