Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
arxiv(2024)
摘要
Sentence transformers are language models designed to perform semantic
search. This study investigates the capacity of sentence transformers,
fine-tuned on general question-answering datasets for asymmetric semantic
search, to associate descriptions of human-generated routes across Great
Britain with queries often used to describe hiking experiences. We find that
sentence transformers have some zero-shot capabilities to understand
quasi-geospatial concepts, such as route types and difficulty, suggesting their
potential utility for routing recommendation systems.
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