GPSBench: Do Large Language Models Understand GPS Coordinates?
arXiv:2602.16105v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or mapping, making robust geospatial reasoning a critical capability. Despite that, LLMs' ability to reason about GPS coordinates and real-world geography remains underexplored. We introduce GPSBench, a dataset of 57,800 samples across 17 tasks for evaluating geospatial reasoning in LLMs, spanning geometric coordinate operations (e.g., distance and bearing computation) and reasoning that integrates coordinates with world knowledge. Focusing on intrinsic model capabilities rather than tool use, we evaluate 14 state-of-the-art LLMs and find that GPS reasoning remains challenging, with substantial variation across tasks: models are generally more reliable at real-world geographic reasoning than at geometric computations. Geographic knowledge degrades hierarchically, with strong country-level performance
arXiv:2602.16105v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or mapping, making robust geospatial reasoning a critical capability. Despite that, LLMs' ability to reason about GPS coordinates and real-world geography remains underexplored. We introduce GPSBench, a dataset of 57,800 samples across 17 tasks for evaluating geospatial reasoning in LLMs, spanning geometric coordinate operations (e.g., distance and bearing computation) and reasoning that integrates coordinates with world knowledge. Focusing on intrinsic model capabilities rather than tool use, we evaluate 14 state-of-the-art LLMs and find that GPS reasoning remains challenging, with substantial variation across tasks: models are generally more reliable at real-world geographic reasoning than at geometric computations. Geographic knowledge degrades hierarchically, with strong country-level performance but weak city-level localization, while robustness to coordinate noise suggests genuine coordinate understanding rather than memorization. We further show that GPS-coordinate augmentation can improve in downstream geospatial tasks, and that finetuning induces trade-offs between gains in geometric computation and degradation in world knowledge. Our dataset and reproducible code are available at https://github.com/joey234/gpsbench
Executive Summary
This study introduces GPSBench, a comprehensive dataset and evaluation framework for assessing the geospatial reasoning capabilities of large language models (LLMs). The researchers investigate 14 state-of-the-art LLMs across 17 tasks, revealing significant variability in model performance. While LLMs excel in real-world geographic reasoning, they struggle with geometric computations. The study also explores the effects of GPS-coordinate augmentation and fine-tuning on geospatial tasks, highlighting trade-offs between gains in geometric computation and degradation in world knowledge. The findings and dataset, available on GitHub, contribute to a deeper understanding of LLMs' geospatial reasoning capabilities and have significant implications for their deployment in applications like navigation and robotics.
Key Points
- ▸ GPSBench is a novel dataset and evaluation framework for assessing LLMs' geospatial reasoning capabilities.
- ▸ LLMs demonstrate significant variability in performance across 17 tasks, with real-world geographic reasoning exceling over geometric computations.
- ▸ GPS-coordinate augmentation can improve performance in downstream geospatial tasks, but fine-tuning induces trade-offs between gains in geometric computation and degradation in world knowledge.
Merits
Comprehensive evaluation framework
The study provides a systematic and extensive evaluation of LLMs' geospatial reasoning capabilities, spanning 17 tasks and including both geometric coordinate operations and reasoning that integrates coordinates with world knowledge.
Insights into LLMs' geospatial reasoning
The findings offer valuable insights into the strengths and weaknesses of LLMs in geospatial reasoning, highlighting areas where models excel and those where they struggle.
Demerits
Limited generalizability
The study focuses on 14 state-of-the-art LLMs, which might not be representative of all LLMs. Additional research is required to confirm the findings and ensure generalizability across different models.
Methodology limitations
The evaluation framework and dataset are designed to assess LLMs' intrinsic capabilities rather than tool use. However, this approach might overlook the importance of tool use in real-world applications.
Expert Commentary
The study demonstrates significant progress in understanding LLMs' geospatial reasoning capabilities and highlights the importance of robust geospatial reasoning in AI models. However, further research is required to address the limitations of the study, including the limited generalizability of the findings and the potential importance of tool use in real-world applications. The GPSBench dataset and evaluation framework provide a valuable resource for the AI research community, enabling a deeper understanding of LLMs' geospatial reasoning capabilities and facilitating the development of more robust and reliable AI models.
Recommendations
- ✓ Future research should focus on developing more comprehensive evaluation frameworks and datasets for assessing LLMs' geospatial reasoning capabilities.
- ✓ Developers and deployers of AI models should prioritize the development and deployment of models with robust geospatial reasoning capabilities for applications like navigation, robotics, and mapping.
