Comparing the Spatial Querying Capacity of Large Language Models: OpenAI’s ChatGPT and Google’s Gemini Pro
Bibliographic record
Abstract
Since the launch of ChatGPT in 2022 and Gemini in 2023, there has been growing interest in the potential application of generative artificial intelligence (AI) in geography and GIScience. As the need for geospatially capable generative AI tools increases, an empirical investigation of generative AI tools’ performance in spatial querying is urgently needed. To fill this gap, we conducted experiments to assess ChatGPT and Gemini regarding their ability to generate accurate answers to spatial queries. The results reveal that ChatGPT and Gemini answered spatial queries to identify neighboring counties as defined by two methods for defining the neighboring relationship between geographical methods (queen contiguity and K-5 nearest neighbors) with accuracies ranging between 49 percent (K-5 with Gemini Pro) and 79 percent (queen with GPT-4). Specifically, GPT-4 outperforms GPT-3.5 and Gemini Pro, and queen contiguity queries yield more accurate answers than K-5 queries. Furthermore, our results show the potential sociodemographic and geographic biases in responses from both ChatGPT and Gemini. In general, the AI models retrieved more accurate answers for counties with larger proportions of urbanized areas and inland counties than their counterparts. Based on these findings, we discuss potential implications for geographers, GIScience researchers, and AI developers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".