DRAGIN: A Novel Machine Learning Framework for Dynamic Retrieval Augmentation in Large Language Models and Outperforming Conventional Methods

The Dynamic Retrieval Augmented Generation (RAG) paradigm aims to improve the performance of LLMs by determining when to retrieve external information and what to retrieve during text generation. Current methods often rely on static rules to decide when to recover and limit retrieval to recent sentences or tokens, which may not capture the full context. […]

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ai paper summary ai shorts applications artificial intelligence augmentation current dynamic editors pick framework information language language model language models large language large language model large language models llms machine machine learning novel paradigm performance rag retrieval retrieval augmented generation rules staff tech news technology text text generation