GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant

TL;DR

GroupGPT is a novel framework that enhances multi-user chat assistants by being token-efficient, privacy-preserving, and capable of handling multimodal inputs through an edge-cloud architecture.

Contribution

It introduces GroupGPT, a new edge-cloud framework for multi-user chat that reduces token usage, preserves privacy, and supports multimodal inputs, along with a benchmark dataset MUIR.

Findings

GroupGPT achieves high response accuracy with an average score of 4.72/5.

Token usage is reduced by up to 3 times compared to baselines.

GroupGPT is well-received in diverse chat scenarios.

Abstract

Recent advances in large language models (LLMs) have enabled increasingly capable chatbots. However, most existing systems focus on single-user settings and do not generalize well to multi-user group chat interactions, where agents require more proactive and accurate intervention under complex, evolving contexts. Existing approaches typically rely on LLMs for both intervention reasoning and response generation, leading to high token consumption, limited scalability, and potential privacy risks. To address these challenges, we propose GroupGPT, a token-efficient and privacy-preserving agentic framework for multi-user chat assistant. GroupGPT adopts an edge-cloud model collaboration architecture to decouple intervention timing from response generation, enabling efficient and accurate decision-making while preserving user privacy through on-device processing of sensitive information. The framework also supports multimodal inputs, including memes, images, videos, and voice messages.To support evaluation of timing accuracy and response quality, we further introduce MUIR, a benchmark dataset for multi-user chat assistant intervention reasoning. MUIR contains 2,500 annotated group chat segments with intervention labels and rationales. We evaluate a range of models on MUIR, spanning from open-source to proprietary variants, including both LLMs and their smaller counterparts. Extensive experiments demonstrate that GroupGPT generates accurate and well-timed responses, achieving an average score of 4.72/5.0 in LLM-based evaluation, and is well-received by users across diverse group chat scenarios. Moreover, GroupGPT reduces the token usage by up to 3 times compared to baselines, while providing privacy sanitization of user messages before cloud transmission. Code is available at: https://github.com/Eliot-Shen/GroupGPT .