OSCAR: Online Soft Compression for RAG

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, leading to improved accuracy and relevance. However, scaling RAG pipelines remains computationally expensive as context length grows. On one hand, hard compression methods have recently proposed to prune the retrieved text on-the-fly with a limited compression ration. On the other hand, soft compression method performs a costly offline compression thanks a dedicated LLM but with a higher compression rate. In this paper, we introduce OSCAR, a novel query-dependent online soft compression method for RAG. OSCAR bridges the gap between online hard and offline soft compression methods, bringing the best of both: OSCAR dynamically compresses retrieved information at inference time, eliminating storage overhead and enabling higher compression rates than existing methods. Our experiments demonstrate state-of-the-art performance with a 2-5x speed-up in inference and minimal, if any, accuracy loss, for LLMs ranging from 1B to 24B parameters.