Comparative Evaluation of Prompting and Fine-Tuning for Applying Large Language Models to Grid-Structured Geospatial Data
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Abstract (arXiv)
This paper presents a comparative study of large language models (LLMs) in interpreting grid-structured geospatial data. We evaluate the performance of a base model through structured prompting and contrast it with a fine-tuned variant trained on a dataset of user-assistant interactions. Our results highlight the strengths and limitations of zero-shot prompting and demonstrate the benefits of fine-tuning for structured geospatial and temporal reasoning.
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References · 6
- 2024The Llama 3 Herd of Modelscited by 14,715
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- 2025A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptationcited by 5
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- 2026MultiGlobeQA: A Multilingual and Globally Diverse Benchmark for Geospatial Reasoning
- 2025From Templates to Natural Language: Generalization Challenges in Instruction-Tuned LLMs for Spatial Reasoning