Title: Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

• Adaptive-RAG addresses the handling of queries with different complexities by LLMs through a dynamic framework.
• A classifier, trained with a smaller LM, predicts query complexity and guides the selection between several augmentation strategies.
• Results on open-domain QA datasets show improved efficiency and accuracy over traditional retrieval approaches.
• The code and approach are shared for replicability and further research.

Opinion: The approach to adaptively handle query complexity through a strategic selection of retrieval-augmented strategies is a notable evolution in the field of question-answering LLMs. Adaptive-RAG could enhance user experience by providing more relevant and accurate responses while maintaining computational efficiency—an essential quality for scalable AI solutions.

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