Paper Detail
Rodrigo Ferreira Rodrigues, Karim Radouane, Jose G Moreno, Lynda Tamine
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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@article{rodrigues2026geobenchllm,
title = {GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks},
author = {Rodrigo Ferreira Rodrigues and Karim Radouane and Jose G Moreno and Lynda Tamine},
year = {2026},
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 \textbackslash{}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 r},
url = {https://arxiv.org/abs/2608.07411},
keywords = {cs.AI, cs.CL, cs.IR, cs.LG},
eprint = {2608.07411},
archiveprefix = {arXiv},
}
{}