The article introduces Adaptive Retrieval-Augmented Generation (Adaptive-RAG), a framework that enhances the performance of Retrieval-Augmented Language Models in Question-Answering (QA) tasks. It addresses a key limitation of existing models, which fail to effectively manage the varying complexities of user queries, often leading to unnecessary computation or inadequate responses. By employing a classifier trained to predict query complexity, Adaptive-RAG can select from different retrieval strategies, optimizing response effectiveness while balancing efficiency. The authors validate their approach across multiple QA datasets, demonstrating significant improvements in accuracy and adaptability.
"Retrieval-Augmented Large Language Models have revolutionized the response accuracy in Question-Answering tasks, but existing methods struggle with varying query complexities, leading to inefficiencies."
"This paper introduces Adaptive-RAG, an automated framework that intelligently selects retrieval strategies based on the predicted complexity level of user queries, enhancing performance across various tasks."
#retrieval-augmented-generation #language-models #query-complexity #question-answering #artificial-intelligence
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