QuIM-RAG retrieves chunks by matching a user question to LLM-generated questions from each chunk in a quantized embedding space, reporting higher QA scores than a traditional RAG baseline on an NDSU website corpus.
Language models are few-shot learners,
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QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
QuIM-RAG retrieves chunks by matching a user question to LLM-generated questions from each chunk in a quantized embedding space, reporting higher QA scores than a traditional RAG baseline on an NDSU website corpus.