Natural Language Processing · free preview
Dense Retrieval and RAG
The vocabulary mismatch problem, bi-encoder dense retrieval with nearest-neighbor search, and retrieval-augmented generation over trusted passages.
1. Dense Retrieval and RAG
Dense Retrieval and RAG
2. Bi-encoder score
A query embedding is and a document embedding is . Compute their dot-product relevance score, rounding to three decimals.
3. Why sparse retrieval missed
A query asks for something to eat and the best passage is a chowder recipe, with no query word appearing in it. What is this failure called, and which fix addresses it?
4. Choosing the encoder arrangement
Why do large-scale systems use a bi-encoder for first-pass retrieval and a cross-encoder only for reranking the top candidates?
5. What retrieval adds over parameters
Which set of problems with prompting a pretrained model does retrieval-augmented generation directly target?
6. Beyond the basic RAG loop
A question needs two facts that live in different documents, and the retrieved set also contains an irrelevant passage. Which two extensions address these issues?
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