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About this paper
Varvara Krechetova, Denis Kochedykov
The GeoBenchX paper introduces a benchmark for evaluating commercial large language models (LLMs) on multi-step geospatial tasks relevant to Geographic Information Systems (GIS) practitioners. It includes over 200 tasks, both solvable and intentionally unsolvable, to assess LLMs' tool-calling capabilities and rejection accuracy, alongside an automated LLM-as-Judge evaluation framework.
Key insights
- Even advanced commercial LLMs have considerable room for improvement in complex, multi-step geospatial reasoning, with the highest success rate for solvable tasks being only slightly above 0.5.
- A notable trade-off exists between an LLM's ability to solve tasks and its capacity to correctly reject unsolvable ones, impacting reliability and the risk of hallucinated outputs.
- LLMs consistently struggle with nuanced geometrical understanding, inefficient data manipulations, and effectively balancing internal world knowledge with provided dataset values for geospatial queries.
Summary by alphaXiv.
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