[Market Trend] What is Retrieval-Augmented Generation (RAG)?




📌 The Key to Advancing Large Language Models

IBM Research Senior Scientist Marina Danilevsky discusses a framework aimed at improving the accuracy and timeliness of large language models called Retrieval-Augmented Generation, or RAG. This framework augments language models with a retrieval function, enabling them to produce text responses to user prompts that are grounded in retrieved data, rather than relying solely on pre-trained information. This method addresses common issues with language models, such as outdated information and lack of sourcing, by ensuring that responses are based on the most current data and primary sources. RAG also enhances the models' ability to admit when they do not have enough information to answer, thus avoiding the provision of misleading information. IBM continues to refine both the retrieval mechanism and the generation process to provide the highest quality responses.


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