MUCA – Notes

In today’s AI Learning session, we had the opportunity to meet Manqing Mao and Jianzhe Lin who co-authored MUCA. Capturing my notes here. There are several interesting ideas in the paper that are applicable to multi-human <-> agent collaboration.

Multi-User Chat Assistant (MUCA): Framework for LLM-Mediated Group Conversations

MUCA targets multi-user, single-agent interactions — a challenging setting where a chatbot must reason not only about what to say but also when and to whom. The system operationalizes these through the 3W design dimensions:

• What – selecting relevant content that advances the discussion or resolves conflicts.
• When – determining optimal response timing to balance engagement without interruption.
• Who – identifying the intended recipient(s) of the response within a group context.

Together, these govern a chatbot’s role as a supportive and context-aware participant in group discussions, rather than a turn-taking speaker responding to each message individually.

Core Modules

1. Sub-topic Generator
   Initializes structured sub-topics from the conversation goal, agenda, or hints, enabling MUCA to guide discussions along coherent and logically connected threads rather than reacting opportunistically to each message.
2. Dialog Analyzer
   Continuously interprets conversation state through several sub-modules:
   • Sub-topic Status Update – tracks whether topics are not discussed, being discussed, or well-discussed, providing situational awareness.
   • Utterance Feature Extractor – identifies which sub-topics are active within the current window, crucial for managing multi-threaded discussions.
   • Accumulative Summary Update – maintains rolling summaries per participant to preserve long-term conversational context efficiently.
   • Participant Feature Extractor – quantifies engagement (frequency, length, and focus of contributions) to detect lurkers or dominant speakers and inform adaptive participation strategies.
3. Utterance Strategies Arbitrator
   Selects one of seven dialog acts, ranked by heuristic confidence and contextual triggers, to determine MUCA’s next move. Each act has trigger conditions, warm-up, and cool-down turns to manage pacing:
   • Direct Chatting: Respond immediately when pinged directly.
   • Initiative Summarization: Periodically generate concise summaries to improve shared understanding.
   • Participation Encouragement: Invite quieter participants to contribute using gentle, personalized prompts.
   • Sub-topic Transition: Detect when a topic is exhausted or stale and guide the group to a new one.
   • Conflict Resolution: Summarize opposing views and propose synthesis or consensus paths.
   • In-context Chime-in: Contribute timely insights or clarifications when conversation flow stalls or questions remain unanswered.
   • Keep Silence: Default behavior to avoid over-participation when no act is warranted, preserving conversational balance.

Design Challenges Addressed

• Stuck Conversation Advancement: Detects stagnation and injects contextually appropriate insights to re-ignite progress.
• Multi-threaded Discussion Management: Tracks overlapping topics and participant clusters to sustain coherence in complex group exchanges.
• Responsiveness Requirement: Maintains timely yet non-intrusive responses despite asynchronous, high-traffic chat environments.
• Participation Evenness: Uses data-driven engagement metrics to encourage balanced contributions across users.
• Conflict Resolution: Applies summarization and consensus-seeking acts to mediate disputes or align diverging viewpoints constructively.

Key Contribution

MUCA provides the first structured framework enabling LLMs to function as facilitators in group settings. By uniting the 3W dimensions, a modular analysis pipeline, and dialog-act arbitration, it transforms large language models from reactive responders into proactive conversation participants capable of maintaining context, inclusivity, and flow in multi-participant discussions

Paper: https://arxiv.org/pdf/2401.04883v1