Precise Zero-Shot Dense Retrieval without Relevance Labels

Authors: Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan (2022)

arXiv: 2212.10496

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TLDR (English)

Makes LLM "pretend" to generate an answer first, then uses its embedding to retrieve real documents. Zero-shot, strong generalization—one of the most reused retrieval enhancement tricks in RAG era.

TLDR（中文）

让 LLM 先"假装"生成一个回答，再用它的 embedding 检索真文档。零监督、强泛化，是 RAG 时代最常被复用的检索增强 trick 之一。

Appears in These Articles

• RAG 与检索增强：让模型有外部记忆
• RAG and Retrieval Augmentation: Giving Models External Memory

Co-cited Papers

These papers appear in the same articles as this one

• Improving language models by retrieving from trillions of tokens Sebastian Borgeaud et al. (2022)
• Dense Passage Retrieval for Open-Domain Question Answering Vladimir Karpukhin et al. (2020)
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis et al. (2020)
• Visual Instruction Tuning Haotian Liu et al. (2023)

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