Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development, while most existing research has primarily centered on single-user chatbots that focus on deciding "What" to answer after user inputs. In this paper, we identified that multi-user chatbots have more complex 3W design dimensions -- "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), which is an LLM-based framework for chatbots specifically designed for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Utterance Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and the appropriate recipients. To make the optimizing process for MUCA easier, we further propose an LLM-based Multi-User Simulator (MUS) that can mimic real user behavior. This enables faster simulation of a conversation between the chatbot and simulated users, making the early development of the chatbot framework much more efficient. MUCA demonstrates effectiveness, including appropriate chime-in timing, relevant content, and positive user engagement, in goal-oriented conversations with a small to medium number of participants, as evidenced by case studies and experimental results from user studies.

翻译：近年来，大型语言模型（LLMs）的进展为聊天机器人开发提供了新途径，但现有研究主要集中在聚焦“用户输入后该回答什么”的单用户聊天机器人上。本文识别出多用户聊天机器人具有更复杂的3W设计维度——“说什么”（What）、“何时回应”（When）及“回答谁”（Who）。我们提出基于LLM的多用户聊天助手（MUCA），这是一个专为群组讨论设计的聊天机器人框架。MUCA包含三个核心模块：子话题生成器、对话分析器与话语策略仲裁器，这些模块共同决定合适的回应内容、时机及目标接收者。为简化MUCA的优化流程，我们进一步提出基于LLM的多用户模拟器（MUS），该模拟器能模拟真实用户行为，从而加速聊天机器人与模拟用户之间的对话模拟，显著提升框架早期开发效率。案例研究与用户实验结果表明，MUCA在中小规模参与者的目标导向对话中展现出有效性，包括恰当的插话时机、相关的内容生成及积极的用户参与度。