GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
Abstract
A new benchmark evaluates large language models on diverse geospatial and temporal tasks, revealing that reasoning ability and model scale strongly influence performance.
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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