Unverified50% confidenceFactExact time
R@1 and Hit@1 are commonly used metrics in retrieval evaluation, referring respectively to the proportion of results where the top-ranked result contains the correct answer and the proportion of relevant documents hit.
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10/27/2026
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UnverifiedThe evaluation metrics include raw Recall@1 (measuring direct retrieval capability) and paired token cosine similarity (measuring the consistency of latent coordinates).67% similarUnverifiedWhen evaluating document information extraction tasks, commonly used metrics include field-level exact match, recall, and end-to-end accuracy.64% similarVerifiedThe pass@1 metric was originally borrowed from the code generation evaluation domain (such as the HumanEval benchmark) to measure the probability of a model generating correct code on the first attempt.64% similarUnverifiedRetrieval benchmarks typically use standard query sets and relevance annotations to evaluate recall, accuracy or ranking quality (such as nDCG, MRR).63% similarVerifiedThe core of RAG evaluation is judging whether what the system "retrieves" and the "answers it generates" truly satisfy user needs, usually comprising two layers: retrieval quality and generation quality.62% similar
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