Equation 18 · Embeddings and the Geometry of Similarity
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Retrieval must therefore be measured on its own terms, against its own labelled relevance judgements, with rank-aware metrics — recall at k for whether the evidence is present at all, and a graded measure such as normalised discounted cumulative gain for whether it is ranked usefully. BEIR’s use of nDCG at 10 across heterogeneous domains is the standard reference for how to do this out of domain [ 20 ] . For embedding models generally, MTEB spans eight task types over 58 datasets and 112 languages, and its headline finding is the one that matters here: across 33 benchmarked models, no single method dominated across all tasks [ 22 ] . There is no best embedding, only a best embedding for a…
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Retrieval must therefore be measured on its own terms, against its own labelled relevance judgements, with rank-aware metrics — recall at k for whether the evidence is present at all, and a graded measure such as normalised discounted cumulative gain for whether it is ranked usefully. BEIR’s use of nDCG at 10 across heterogeneous domains is the standard reference for how to do this out of domain [ 20 ] . For embedding models generally, MTEB spans eight task types over 58 datasets and 112 languages, and its headline finding is the one that matters here: across 33 benchmarked models, no single method dominated across all tasks [ 22 ] . There is no best embedding, only a best embedding for a task and a corpus.
Sources cited in the surrounding passage
- [20] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models ↗
- [22] MTEB: Massive Text Embedding Benchmark ↗
These citations give research context. Read each source to check which claims it supports.