Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation

TL;DR

FlexRAG introduces a flexible, cost-effective method for retrieval-augmented generation by compressing retrieved contexts into optimized embeddings, improving quality and efficiency across diverse tasks.

Contribution

The paper proposes FlexRAG, a novel approach that compresses and optimizes retrieved contexts for better performance and flexibility in RAG systems.

Findings

Achieves higher quality generation with lower computational cost.

Supports diverse compression ratios and context preservation.

Validated on multiple question-answering datasets.

Abstract

The existing Retrieval-Augmented Generation (RAG) systems face significant challenges in terms of cost and effectiveness. On one hand, they need to encode the lengthy retrieved contexts before responding to the input tasks, which imposes substantial computational overhead. On the other hand, directly using generic Large Language Models (LLMs) often leads to sub-optimal answers, while task-specific fine-tuning may compromise the LLMs' general capabilities. To address these challenges, we introduce a novel approach called FlexRAG (Flexible Context Adaptation for RAG). In this approach, the retrieved contexts are compressed into compact embeddings before being encoded by the LLMs. Simultaneously, these compressed embeddings are optimized to enhance downstream RAG performance. A key feature of FlexRAG is its flexibility, which enables effective support for diverse compression ratios and selective preservation of important contexts. Thanks to these technical designs, FlexRAG achieves superior generation quality while significantly reducing running costs. Comprehensive experiments on various question-answering datasets validate our approach as a cost-effective and flexible solution for RAG systems.