RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation

arXiv:2404.00610v1 Announce Type: new
Abstract: Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. To tackle these challenges, Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process, thus leveraging non-parametric knowledge alongside LLMs' in-context learning abilities. However, existing RAG implementations primarily focus on initial input for context retrieval, overlooking …

arxiv cs.cl queries rag refine retrieval retrieval augmented generation type