Measuring Geographic Diversity of Foundation Models with a Natural Language--based Geo-guessing Experiment on GPT-4

TL;DR

This study evaluates GPT-4's geographic knowledge using a natural language geo-guessing experiment, revealing regional disparities and limitations in representing geographic features globally and locally.

Contribution

It introduces a novel natural language-based geo-guessing method to assess the geographic diversity of GPT-4's knowledge, highlighting regional disparities and modality differences.

Findings

GPT-4 encodes insufficient geographic knowledge globally.

Regional disparities exist in GPT-4's geo-guessing performance.

Inter-model performance varies between unimodal and multimodal GPT-4 variants.

Abstract

Generative AI based on foundation models provides a first glimpse into the world represented by machines trained on vast amounts of multimodal data ingested by these models during training. If we consider the resulting models as knowledge bases in their own right, this may open up new avenues for understanding places through the lens of machines. In this work, we adopt this thinking and select GPT-4, a state-of-the-art representative in the family of multimodal large language models, to study its geographic diversity regarding how well geographic features are represented. Using DBpedia abstracts as a ground-truth corpus for probing, our natural language--based geo-guessing experiment shows that GPT-4 may currently encode insufficient knowledge about several geographic feature types on a global level. On a local level, we observe not only this insufficiency but also inter-regional disparities in GPT-4's geo-guessing performance on UNESCO World Heritage Sites that carry significance to both local and global populations, and the inter-regional disparities may become smaller as the geographic scale increases. Morever, whether assessing the geo-guessing performance on a global or local level, we find inter-model disparities in GPT-4's geo-guessing performance when comparing its unimodal and multimodal variants. We hope this work can initiate a discussion on geographic diversity as an ethical principle within the GIScience community in the face of global socio-technical challenges.