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About this paper
Simranjit Singh, Michael Fore, Dimitrios Stamoulis
GeoLLM-Engine provides a realistic environment for building and evaluating geospatial Copilots, utilizing a largely human-out-of-the-loop process for generating over half a million complex tasks with verified ground truths. Evaluations reveal GPT-4 Turbo substantially outperforms GPT-3.5, and task complexity, rather than benchmark size, is critical for assessing agent capabilities in Earth Observation workflows.
Key insights
- GPT-4 Turbo demonstrates substantially superior performance over GPT-3.5 Turbo across various geospatial task metrics.
- Agent success rates inversely correlate with task complexity, with performance dropping significantly for tasks requiring more tool calls, irrespective of benchmark size.
- ReAct prompting proves more effective than Chain-of-Thought (CoT) or Chameleon for enabling LLMs to handle complex, multi-tool geospatial commands.
- The primary source of errors in GPT-4 agents is frequently 'Missed Function' calls, highlighting a need for improved tool orchestration.
Summary by alphaXiv.
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