GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

Abstract:Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. This omission is critical as retrieval oriented geo-analytic tasks cannot be supported reliably without first identifying the necessary datasets and constraints. GeoFitBench-LLM is introduced as a benchmark for assessing whether LLMs can infer the datasets, the supporting resources and the constraints required by geo-analytic requests in natural language. The benchmark is structured around two reasoning dimensions, Spatio-Temporal Relational and Semantic Grounding, reflecting long-standing challenges in geospatial information retrieval and related natural language tasks. Ground truth requirements are encoded in a fixed, provider-independent schema for reproducible evaluation of resource identification and attribute-level specification. Experiments on seven models under minimal prompting indicate that identifying the necessary resources is often achievable, with GPT-5.4 Thinking achieving the best result by recovering the complete set of resources in 66.67% of benchmark requests. Results further indicate that accurate fitness-for-use specification remains more difficult as inferential demands increase.

Presented as an Oral Presentation Paper at GeoAI 2026, Ghent, Belgium. Also available on Zenodo

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.12669 [cs.CL]

(or arXiv:2610.12669v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.12669 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.5281/zenodo.20129011 Focus to learn more DOI(s) linking to related resources

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