The article presents the Adaptive Retrieval-Augmented Generation (Adaptive-RAG) framework, which adapts to varying query complexities by employing different strategies. It classifies queries into three types: non-retrieval-based for simple queries, single-step for moderate complexities, and multi-step for complex queries. The framework's effectiveness was validated through tests on various open-domain QA datasets, showcasing significant improvements in accuracy and efficiency compared to standard methods. A key aspect of Adaptive-RAG is its ability to determine query complexity using a trained language model, optimizing resource allocation for improved responses.
The Adaptive-RAG framework optimally adjusts query handling strategies based on complexity, ranging from non-retrieval methods for simple queries to multi-step strategies for complex ones.
Our results demonstrate that Adaptive-RAG significantly enhances QA system accuracy and efficiency, effectively allocating resources based on query complexity and improving performance over traditional methods.
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