GCAgent: Enhancing Group Chat Communication through Dialogue Agents System

2026-03-05

AI Summary

This study addresses the inefficiencies in group chats caused by low engagement and management challenges, which existing large language models (LLMs) struggle to mitigate due to their limited support for multi-participant conversations. To bridge this gap, the work proposes a novel LLM-driven agent system specifically designed for group chat environments, comprising three core components: an Agent Builder that customizes agents based on user interests, a Dialogue Manager that coordinates conversation states, and lightweight Interface Plugins that enable seamless interaction. Experimental results demonstrate that the proposed system outperforms baseline approaches in 51.04% of evaluated scenarios, achieving an average user rating of 4.68. Furthermore, during a real-world deployment spanning 350 days, the system increased message volume by 28.80%, significantly enhancing both interactivity and practical utility in group communication.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 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.

Problem

• group chat
• large language models
• multi-party conversation
• dialogue agents
• user engagement

Innovation

• Group Chat
• Large Language Models
• Dialogue Agents
• Multi-party Conversation
• Agent System