s3: The new RAG framework that trains search agents with minimal data
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Summary
Researchers from the University of Illinois Urbana-Champaign have developed an open-source framework called s3, which reduces the cost of developing retrieval models for retrieval-augmented generation (RAG) systems.
Current RAG systems are designed to improve the performance of large language models by giving them access to external knowledge and enabling them to ask questions based on that knowledge to provide more accurate and contextually relevant answers.
However, they require fine-tuning, which can be expensive and error-prone.
s3 uses reinforcement learning to train a search agent with iterative, multi-turn access to external knowledge, improving the quality of the retrieval stage without impacting the model that generates the final answer.
The researchers claim that s3 offers advantages over existing RAG methods in terms of data efficiency, domain adaptability and optimisation strategy, meaning it could be applied in healthcare, knowledge management and scientific research to enhance retrieval quality while requiring less training data.