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The Cartographer
GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
August 07, 2026 Β· Grace Period Β· π CIKM2026
Authors
Rodrigo Ferreira Rodrigues, Karim Radouane, Jose G Moreno, Lynda Tamine
arXiv ID
2608.07411
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CL,
cs.IR,
cs.LG
Citations
0
Venue
CIKM2026
Abstract
In the context of geodata, existing Large Language Models have often been studied in a homogeneous setting, which has considerably limited insights into their generalization capabilities. In this paper, we present \benchName, a comprehensive benchmark for probing LLMs on geo-related tasks. We leverage a careful selection of twelve publicly available datasets from diverse geo-related tasks and domains, and evaluate a set of LLMs on geo-spatial and temporal understanding using our benchmark. Our results show that reasoning and size have a strong impact on overall performance. GeoBenchLLM is publicly available at https://github.com/Rfr2003/GeoBenchLLM.
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