How can I restructure retrieved content for better LLM QA?
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
This paper introduces Refiner, a new component for Retrieval-Augmented Generation (RAG) systems designed to improve the quality of answers generated by Large Language Models (LLMs). Refiner analyzes the retrieved documents, extracts relevant information, structures it into sections based on relatedness, and feeds this structured content to the LLM. This helps LLMs avoid getting confused by irrelevant or contradictory information, especially in complex, multi-step reasoning tasks. The key improvements relevant to LLM-based multi-agent systems are Refiner's ability to handle multiple sources of information (like multiple agents), its focus on structuring and organizing this information (facilitating inter-agent communication), and its improvement of LLM performance by focusing on relevant data (improving agent effectiveness).