AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models

Understand

Retrieved documents containing noise will hinder RAG from detecting answer clues and make the inference process slow and expensive.

• Therefore, context compression is necessary to enhance its accuracy and efficiency.
• Existing context compression methods use extractive or generative models to retain the most query-relevant sentences or apply the information bottleneck theory to preserve sufficient information.
• However, these methods may face issues such as over-compression or high computational costs.