用AI助手提升群聊活跃度，让多人对话更有趣、更高效。
GCAgent: Enhancing Group Chat Communication through Dialogue Agents System
- 设计三模块系统，自动匹配用户兴趣并管理对话流程
- 实测群聊消息量提升28.8%，用户偏好度超基线51%
- 适合想提升社群互动的平台或团队使用
作为在线社交平台的重要形式，群聊是兴趣交流与问题解决的热门空间，但常因成员不活跃和管理困难而效率低下。尽管大语言模型（LLMs）已在一对一对话中表现优异，其在多参与者对话中的无缝集成仍待探索。为此，我们提出GCAgent，一个基于LLM的群聊增强系统，包含娱乐与实用双重功能的对话代理。系统由三个紧密协作模块构成：Agent Builder根据用户兴趣定制代理；Dialogue Manager协调对话状态并管理代理调用；Interface Plugins通过三种工具降低交互门槛。大规模实验显示，GCAgent在各项指标上平均得分4.68，且在51.04%的对比中更受青睐。在350天的真实部署中，消息量提升28.80%，显著增强群组活跃度与参与感。本工作为将基于LLM的对话代理从单人扩展至多人群聊场景提供了可行蓝图。
原文摘要 · Abstract (English)
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 score of 4.68 across various criteria and was preferred in 51.04\% of cases compared to its base model. Additionally, in real-world deployments over 350 days, it increased message volume by 28.80\%, significantly improving group activity and engagement. Overall, this work presents a practical blueprint for extending LLM-based dialogue agent from one-party chats to multi-party group scenarios.
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