Retrieval
Bilgi getirme
D3
Retrieval is the process of selecting information items from a collection for use in response to a query or task.
Review status: 2027-08-28
Technical explanation
A retriever can select a small candidate set of passages before another component reads, ranks, or uses those passages to produce an answer.
Conceptual boundaries
Retrieval is not generation and does not require vector embeddings, because systems can use sparse, dense, or other retrieval methods.
Provider-neutral example
Given an employee question, a policy search service can retrieve five candidate sections before a reviewer or model prepares a response.
Limitations
The cited evidence is limited to open-domain question answering and does not show that one sparse or dense retrieval method is universally best.
Related concepts
Atomic claims and evidence
1.1Karpukhin et al., Dense Passage Retrieval for Open-Domain Question Answering
- Source
- Karpukhin et al., Dense Passage Retrieval for Open-Domain Question Answering
- Source role
- Authoritative source
- Exact locator
- Section 1: two-stage framework with context retriever and machine reader
- Supported claim
- In open-domain question answering, a context retriever selects a small subset of passages before a reader examines the retrieved contexts.
- Last verification
- Review due
- Scope limitation
- This describes a two-stage question-answering framework and does not define every retrieval application.
2.1Karpukhin et al., Dense Passage Retrieval for Open-Domain Question Answering
- Source
- Karpukhin et al., Dense Passage Retrieval for Open-Domain Question Answering
- Source role
- Authoritative source
- Exact locator
- Abstract; Section 1: sparse TF-IDF/BM25 and dense representations
- Supported claim
- Retrieval can use sparse keyword methods such as TF-IDF or BM25 as well as dense representations.
- Last verification
- Review due
- Scope limitation
- This names methods discussed in the cited research and does not rank one method as universally best.