Unverified50% confidenceFactExact time
In the single-document scenario, hit rate and recall@k trend consistently; under multi-document queries, hit rate is naturally biased toward any-hit logic, while recall@k calculated as 'number of relevant documents recalled / total number of relevant documents' is closer to the all-evidence perspective.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRecall@K indicates what proportion of truly relevant documents were successfully found among the top K results returned by the system.72% similarUnverifiedrecall@k refers to the proportion of questions for which the top k results contain at least one correct text chunk.71% similarUnverifiedIn retrieval evaluation, Recall@k is prioritized, because missing evidence leaves downstream generation with no way to recover, whereas bringing back a small amount of noisy documents can usually still be filtered out by the context window or a reranker.63% similarUnverifiedIn the field of information retrieval, recall refers to the proportion of all relevant information that the system finds, while precision refers to the proportion of the information returned by the system that is truly relevant.60% similarUnverifiedWhen a retriever recalls documents based solely on semantic similarity, it may return both outdated and current documents simultaneously, causing the LLM to answer based on stale information.60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1048180API
curl https://kongchang.com/api/v1/knowledge/claims/1048180MCP
get_claim(id=1048180)