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Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations

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arxiv 2401.04883 v4 pith:UBDHNXBD submitted 2024-01-10 cs.CL

Manqing Mao , Paishun Ting , Yijian Xiang , Mingyang Xu , Julia Chen , Jianzhe Lin This is my paper · ORCID
classification cs.CL
keywords mucamulti-userconversationsanswerappropriateassistantchatchatbot
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development. Most existing research, however, has primarily centered on single-user chatbots that determine "What" to answer. This paper highlights the complexity of multi-user chatbots, introducing the 3W design dimensions: "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), an LLM-based framework tailored for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Conversational Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and appropriate addressees. This paper further proposes an LLM-based Multi-User Simulator (MUS) to ease MUCA's optimization, enabling faster simulation of conversations between the chatbot and simulated users, and speeding up MUCA's early development. In goal-oriented conversations with a small to medium number of participants, MUCA demonstrates effectiveness in tasks like chiming in at appropriate timings, generating relevant content, and improving user engagement, as shown by case studies and user studies.

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Cited by 4 Pith papers

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    cs.AI 2026-07 conditional novelty 6.0 of 10

    A training-free caching and per-group FP8 quantization framework reports ~1.3× standalone and up to 40.2× combined speedups for diffusion LLM inference while keeping benchmark accuracy roughly stable.

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    cs.CL 2025-02 conditional novelty 6.0 of 10

    LLM meeting delegates achieve about 60% loose recall on a new benchmark built from real meeting transcripts, with GPT-4/4o most balanced.

  3. Point of Interest Recommendation: Pitfalls and Viable Solutions

    cs.IR 2025-07 accept novelty 4.0 of 10

    A reflection paper argues that POI recommendation research is held back by 20 systemic pitfalls in datasets, algorithms, and evaluation, and outlines six solutions.

  4. Generative Intelligence Systems in the Flow of Group Emotions

    cs.HC 2025-07 reject novelty 4.0 of 10

    An unvalidated reference architecture for multi-agent generative orchestration of group emotion contagion, presented as a patent-style description with no empirical results.

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