GCAgent: Enhancing Group Chat Communication through Dialogue Agents System
Zijie Meng, Zheyong Xie, Zheyu Ye, Chonggang Lu, Zuozhu Liu, Zihan Niu, Yao Hu, Shaosheng Cao

TL;DR
GCAgent is a novel system that integrates large language models into group chat platforms, improving engagement and management through specialized dialogue agents and interface tools, validated by extensive experiments and real-world deployment.
Contribution
This work introduces GCAgent, the first system to seamlessly incorporate LLM-driven dialogue agents into multi-participant group chats, enhancing communication and activity.
Findings
Achieved an average score of 4.68 across various criteria.
Preferred in 51.04% of cases over the base model.
Increased message volume by 28.80% in real-world deployment.
Abstract
As a key form in online social platforms, group chat is a popular space for interest exchange or problem-solving, but its effectiveness is often hindered by inactivity and management challenges. While recent large language models (LLMs) have powered impressive one-to-one conversational agents, their seamlessly integration into multi-participant conversations remains unexplored. To address this gap, we introduce GCAgent, an LLM-driven system for enhancing group chats communication with both entertainment- and utility-oriented dialogue agents. The system comprises three tightly integrated modules: Agent Builder, which customizes agents to align with users' interests; Dialogue Manager, which coordinates dialogue states and manage agent invocations; and Interface Plugins, which reduce interaction barriers by three distinct tools. Through extensive experiment, GCAgent achieved an average…
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Taxonomy
TopicsSpeech and dialogue systems · AI in Service Interactions · Topic Modeling
