GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
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Computer Science > Artificial Intelligence
Title:GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
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 this https URL.
| Comments: | Accepted at CIKM2026 |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.07411 [cs.AI] |
| (or arXiv:2608.07411v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07411
arXiv-issued DOI via DataCite (pending registration)
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