Microsoft AI, in partnership with Renmin University of China, has developed Chain-of-Retrieval Augmented Generation (CoRAG), an advanced AI framework to improve traditional Retrieval-Augmented Generation (RAG) models. CoRAG introduces a dynamic query reformulation mechanism that allows for iterative search and reasoning. This addresses the limitations of traditional RAG systems, particularly in handling complex queries in multi-hop question answering. By refining queries multiple times based on prior reasoning, CoRAG retrieves contextually relevant information, ultimately leading to more accurate and comprehensive answers. Training through rejection sampling further enhances the model's capabilities.
CoRAG enables iterative search and reasoning in Retrieval-Augmented Generation models, allowing AI to refine its retrievals dynamically before generating answers.
The dynamic query reformulation mechanism of CoRAG improves retrieval processes by ensuring each retrieved piece of information is contextually relevant.
Unlike traditional models, CoRAG's ability to 'think through' retrievals enhances multi-hop question answering by reformulating queries at each stage.
CoRAG utilizes rejection sampling for training on enhanced datasets, allowing it to generate sub-queries and sub-answers without expensive human annotations.
#ai #retrieval-augmented-generation #machine-learning #query-reformulation #natural-language-processing
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