Adaptive Re-Ranking trains a classifier to route queries to BM25, MiniLM-L6-v2, or BGE-v2-m3 based on a utility label, yielding 1.15-53x lower median latency and competitive nDCG@10 versus always using the heaviest model.
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Sentence transformers show partial zero-shot ability to link route descriptions with hiking queries, indicating some grasp of quasi-geospatial concepts like type and difficulty.
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Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Sentence transformers show partial zero-shot ability to link route descriptions with hiking queries, indicating some grasp of quasi-geospatial concepts like type and difficulty.