GeoBenchLLM: A Comprehensive Benchmark for Evaluating LLMs on Geo-Related Tasks
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Abstract (arXiv)
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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References · 11
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
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- 2018Improving Text-to-SQL Evaluation Methodologycited by 135
- 2022StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Textscited by 68
- 2023GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendationcited by 58
- 2023Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoningcited by 49
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- 2021Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directionscited by 24
- 2019GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Levelcited by 17
- 2025MapQA: Open-domain Geospatial Question Answering on Map Datacited by 15
- 2021When Retriever-Reader Meets Scenario-Based Multiple-Choice Questionscited by 3
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