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        <title>Coordinates from Context: Using LLMs to Ground Complex Location References</title>
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        <namePart type="given">Tessa</namePart>
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        <name type="personal">
            <namePart type="given">Vera</namePart>
            <namePart type="family">Demberg</namePart>
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    <abstract>Geocoding is the task of linking a location reference to an actual geographic location and is essential for many downstream analyses of unstructured text. In this paper, we explore the challenging setting of geocoding compositional location references. Building on recent work demonstrating LLMs’ abilities to reason over geospatial data, we evaluate LLMs’ geospatial knowledge versus reasoning skills relevant to our task. Based on these insights, we propose an LLM-based strategy for geocoding compositional location references. We show that our approach improves performance for the task and that a relatively small fine-tuned LLM can achieve comparable performance with much larger off-the-shelf models.</abstract>
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%0 Conference Proceedings
%T Coordinates from Context: Using LLMs to Ground Complex Location References
%A Masis, Tessa
%A O’Connor, Brendan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F masis-o-connor-2026-coordinates
%X Geocoding is the task of linking a location reference to an actual geographic location and is essential for many downstream analyses of unstructured text. In this paper, we explore the challenging setting of geocoding compositional location references. Building on recent work demonstrating LLMs’ abilities to reason over geospatial data, we evaluate LLMs’ geospatial knowledge versus reasoning skills relevant to our task. Based on these insights, we propose an LLM-based strategy for geocoding compositional location references. We show that our approach improves performance for the task and that a relatively small fine-tuned LLM can achieve comparable performance with much larger off-the-shelf models.
%U https://aclanthology.org/2026.eacl-long.74/
%P 1591-1606

Markdown (Informal)

[Coordinates from Context: Using LLMs to Ground Complex Location References](https://aclanthology.org/2026.eacl-long.74/) (Masis & O'Connor, EACL 2026)

• Coordinates from Context: Using LLMs to Ground Complex Location References (Masis & O’Connor, EACL 2026)

ACL

• Tessa Masis and Brendan O'Connor. 2026. Coordinates from Context: Using LLMs to Ground Complex Location References. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1591–1606, Rabat, Morocco. Association for Computational Linguistics.