LevelRAG introduces a novel approach that separates query planning from retrieval mechanisms in Retrieval-Augmented Generation (RAG) systems, significantly reducing hallucinations in Large Language Models.

• Decouples complex query logic from the retrieval process
• Employs a multi-hop logic planning system that works across diverse search methods
• Demonstrates superior performance in handling complex reasoning tasks requiring external knowledge
• Achieves better factual accuracy while maintaining computational efficiency

From a security perspective, LevelRAG's approach directly addresses the critical issue of hallucinations in LLMs, enhancing trustworthiness and reliability when these models are deployed in sensitive contexts requiring factual accuracy.

LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers