  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 improving user engagement, in group conversations with a small to
medium number of participants, as evidenced by case studies and experimental
results from user studies.
