GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
arXiv:2603.01059v1 Announce Type: new 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 chats, where agents require more proactive and accurate intervention under complex, evolving contexts. Existing approaches typically rely on LLMs for both reasoning and 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 a small-large model collaborative architecture to decouple intervention timing from response generation, enabling efficient and accurate decision-making. The framework also supports multimodal inputs, including memes, images, videos, and voice messages. We further introduce MUIR, a benchmark dataset for multi-user chat assistan
arXiv:2603.01059v1 Announce Type: new 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 chats, where agents require more proactive and accurate intervention under complex, evolving contexts. Existing approaches typically rely on LLMs for both reasoning and 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 a small-large model collaborative architecture to decouple intervention timing from response generation, enabling efficient and accurate decision-making. The framework also supports multimodal inputs, including memes, images, videos, and voice messages. 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, supporting evaluation of timing accuracy and response quality. We evaluate a range of models on MUIR, from large language models to smaller counterparts. Extensive experiments demonstrate that GroupGPT produces 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 token usage by up to 3 times compared to baseline methods, while providing privacy sanitization of user messages before cloud transmission. Code is available at: https://github.com/Eliot-Shen/GroupGPT .
Executive Summary
This article introduces GroupGPT, a token-efficient and privacy-preserving agentic framework for multi-user chat assistant. The framework adopts a small-large model collaborative architecture, enabling efficient and accurate decision-making. GroupGPT also supports multimodal inputs and reduces token usage by up to 3 times compared to baseline methods. The authors evaluate GroupGPT on the MUIR benchmark dataset and demonstrate its accuracy and well-timed responses. The framework is also well received by users across diverse group chat scenarios. Furthermore, GroupGPT provides privacy sanitization of user messages before cloud transmission, addressing potential privacy risks. The authors' approach has the potential to advance the development of AI-powered chat assistants and improve user experience in multi-user settings.
Key Points
- ▸ GroupGPT is a token-efficient and privacy-preserving agentic framework for multi-user chat assistant
- ▸ The framework adopts a small-large model collaborative architecture for efficient and accurate decision-making
- ▸ GroupGPT supports multimodal inputs, including memes, images, videos, and voice messages
Merits
Strength in Addressing Scalability Challenges
GroupGPT's collaborative architecture and token-efficient design enable it to scale more effectively than traditional large language models, making it a promising solution for multi-user chat settings.
Innovative Approach to Privacy Preservation
GroupGPT's privacy sanitization mechanism provides a secure and private way to transmit user messages, addressing a significant limitation of traditional chat assistants.
Demerits
Limited Generalizability to Real-World Scenarios
The authors' evaluation of GroupGPT is primarily based on a benchmark dataset, which may not fully capture the complexities and nuances of real-world group chat settings.
Dependence on Large Language Model Performance
GroupGPT's success relies heavily on the performance of the underlying large language model, which may be subject to variability and bias.
Expert Commentary
The introduction of GroupGPT represents a significant advancement in the development of AI-powered chat assistants. By decoupling intervention timing from response generation, the framework enables more efficient and accurate decision-making in multi-user chat settings. The authors' innovative approach to privacy preservation is also a welcome contribution to the ongoing conversation about the need for greater transparency and security in AI systems. However, as with any new technology, there are potential limitations and challenges that need to be addressed. For example, the dependence on large language model performance and the limited generalizability to real-world scenarios are important considerations for future research and development. Nevertheless, GroupGPT offers a promising solution for improving the performance and scalability of AI-powered chat assistants, and its implications for both practical and policy-making contexts are significant.
Recommendations
- ✓ Recommendation 1: Further research is needed to fully explore the potential of GroupGPT and to address the limitations and challenges identified in this article.
- ✓ Recommendation 2: The development and deployment of GroupGPT and similar frameworks should be accompanied by policy and regulatory frameworks to ensure the protection of user data and privacy.