Contextualized token retriever
Topics: Chunk Relevance, Indexing, Learning-to-rank, LLM Readability, LLMO / GEO, Passage based retrieval, Ranking, Retrieval Augmented Generation (RAG), Scoring, Semantic Search
This Google patent describes a retrieval system that finds documents relevant to a query by encoding queries and documents into token-level vectors and, crucially, scoring documents using only the subset of document token vectors that were retrieved as most similar to each query token (rather than using all token vectors of each document). The approach uses per-query-token nearest-neighbor retrieval, per-token query scores (with imputation for missing matches at inference), aggregation into a document relevance score, and training procedures that avoid imputation. The result is claimed to keep token-level expressivity while drastically reducing compute and memory cost and improving retrieval accuracy.
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