Retrieval Augmented Retrieval with In Context Examples
The paper "**Retrieval Augmented Retrieval**" introduces an innovative approach to enhance information retrieval in Large Language Models through *recursive retrieval augmentation*. The authors present a novel framework where retrieved documents are used to find additional relevant in-context examples, creating a more comprehensive context for query processing. The system employs a **two-stage retrieval process**: first retrieving relevant documents, then using these to guide the selection of appropriate in-context examples. This *recursive approach* significantly improves retrieval accuracy by providing richer contextual information to the LLM. The research demonstrates substantial performance improvements across various tasks, including *question answering*, *fact verification*, and *information synthesis*. Results show that this method achieves up to 25% better accuracy compared to traditional retrieval-augmented generation approaches. The paper also explores the impact of different retrieval strategies and the optimal number of recursive steps for various applications.
