Study reveals tension between a LLM's prior knowledge and reference data

A study from Stanford University investigates the extent to which Retrieval Augmented Generation (RAG) improves the factual accuracy of Large Language Models (LLMs). The results show that the reliability of RAG systems depends critically on the quality of the data sources used, and that prior knowledge of the language model matters.

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accuracy ai research artificial intelligence data data sources knowledge language language models large language large language models llm llms prior quality rag reference reliability results retrieval retrieval augmented generation show stanford stanford university study systems university