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arXiv · 2505.17116

Comparative Evaluation of Prompting and Fine-Tuning for Applying Large Language Models to Grid-Structured Geospatial Data

Akash Dhruv·Yangxinyu Xie·Jordan Branham·Tanwi Mallick
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Abstract

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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Akash Dhruv, Yangxinyu Xie, Jordan Branham, Tanwi Mallick. 2025-05-21. Comparative Evaluation of Prompting and Fine-Tuning for Applying Large Language Models to Grid-Structured Geospatial Data. https://arxiv.org/abs/2505.17116

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