Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction

• URL: http://arxiv.org/abs/2308.04215v3
• Date: Sat, 12 Oct 2024 12:50:33 GMT
• Title: Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction
• Authors: Menglin Xia, Xuchao Zhang, Camille Couturier, Guoqing Zheng, Saravan Rajmohan, Victor Ruhle,
• Abstract summary: We propose Hybrid Retrieval-Augmented Composition Assistance (Hybrid-RACA) for real-time text prediction. It efficiently combines a cloud-based large language model with a smaller client-side model through retrieval augmented memory. Our experiments on five datasets demonstrate that Hybrid-RACA offers strong performance while maintaining low latency.
• Score: 17.94189417448127
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) enhanced with retrieval augmentation has shown great performance in many applications. However, the computational demands for these models pose a challenge when applying them to real-time tasks, such as composition assistance. To address this, we propose Hybrid Retrieval-Augmented Composition Assistance (Hybrid-RACA), a novel system for real-time text prediction that efficiently combines a cloud-based LLM with a smaller client-side model through retrieval augmented memory. This integration enables the client model to generate better responses, benefiting from the LLM's capabilities and cloud-based data. Meanwhile, via a novel asynchronous memory update mechanism, the client model can deliver real-time completions to user inputs without the need to wait for responses from the cloud. Our experiments on five datasets demonstrate that Hybrid-RACA offers strong performance while maintaining low latency.

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