# Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?

Source: [Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?](<https://www.sota2.com/research/paper/68fa72d51e209131cea58cc1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-08T05:00:04.262Z

Authors: Ilya Ilyankou, Aldo Lipani, Stefano Cavazzi, Xiaowei Gao, James Haworth

Published: 2024-04-05T15:22:02.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.04169>)

[PDF](<https://arxiv.org/pdf/2404.04169.pdf>)