[2504.07109] OSCAR: Online Soft Compression And Reranking

Computer Science > Information Retrieval arXiv:2504.07109 (cs) [Submitted on 17 Mar 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:OSCAR: Online Soft Compression And Reranking Authors:Maxime Louis, Thibault Formal, Hervé Dejean, Stéphane Clinchant View a PDF of the paper titled OSCAR: Online Soft Compression And Reranking, by Maxime Louis and 3 other authors View PDF HTML (experimental) Abstract: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 retrieval sizes grow. To address this, we introduce OSCAR, a novel query-dependent online soft compression method that reduces computational overhead while preserving performance. Unlike traditional hard compression methods, which shorten retrieved texts, or soft compression approaches, which map documents to continuous embeddings offline, OSCAR dynamically compresses retrieved information at inference time, eliminating storage overhead and enabling higher compression rates. Additionally, we extend OSCAR to simultaneously perform reranking, further optimizing the efficiency of the RAG pipeline. Our experiments demonstrate state-of-the-art performance with a 2-5x speed-up in inference and minimal to no loss in accuracy for LLMs ranging from 1B to 24B parameters. The models are available at: this https URL. Subjects: Information Retrieval (cs.IR); Arti...