Comparing the Spatial Querying Capacity of Large Language Models: OpenAI’s ChatGPT and Google’s Gemini Pro
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it