Auto-RAG: An Autonomous Iterative Retrieval Model Centered on the LLM’s Powerful Decision-Making Capabilities

Retrieval Augmented Generation is an efficient solution for knowledge-intensive tasks that improves the quality of outputs and makes it more deterministic with minimal hallucinations. However, RAG outputs can still be noisy and may fail to respond appropriately to complex queries. To address this limitation, iterative retrieval updates have been introduced, which update re-retrieval results to […]

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