Large Language Models are Geographically Biased

1 code implementation • 5 Feb 2024 • Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, Stefano Ermon

Initially, we demonstrate that LLMs are capable of making accurate zero-shot geospatial predictions in the form of ratings that show strong monotonic correlation with ground truth (Spearman's $\rho$ of up to 0. 89).

Fairness

GeoLLM: Extracting Geospatial Knowledge from Large Language Models

1 code implementation • 10 Oct 2023 • Rohin Manvi, Samar Khanna, Gengchen Mai, Marshall Burke, David Lobell, Stefano Ermon

With GeoLLM, we observe that GPT-3. 5 outperforms Llama 2 and RoBERTa by 19% and 51% respectively, suggesting that the performance of our method scales well with the size of the model and its pretraining dataset.