License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.43
URN: urn:nbn:de:0030-drops-189381
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18938/
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Ji, Yuhan ; Gao, Song

Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper)

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LIPIcs-GIScience-2023-43.pdf (0.6 MB)

Abstract

This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated embeddings for geometric attributes. The experiments demonstrate that while the LLMs-generated embeddings can preserve geometry types and capture some spatial relations (up to 73% accuracy), challenges remain in estimating numeric values and retrieving spatially related objects. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using foundation models.

BibTeX - Entry

@InProceedings{ji_et_al:LIPIcs.GIScience.2023.43,
  author =	{Ji, Yuhan and Gao, Song},
  title =	{{Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{43:1--43:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18938},
  URN =		{urn:nbn:de:0030-drops-189381},
  doi =		{10.4230/LIPIcs.GIScience.2023.43},
  annote =	{Keywords: LLMs, foundation models, GeoAI}
}

Keywords: LLMs, foundation models, GeoAI
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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