LLMs Flatten the Global South: Sub-Regional Representation Asymmetry
Noah De Nicola
LLMs default to Northern values and collapse within-country variation. Prior work shows this at the output level. We ask whether the same asymmetry shows up in the model's internal representations, with sub-regions in the Global South separating less than those in the Global North. We build persona vectors for 21 countries and 105 cities across three open-weight LLMs, and measure how far each country's cities separate from it in activation space. We control for two confounds: Wikipedia corpus frequency and BPE tokenizer fragmentation. Global North sub-regions separate 39% more than Global South across all layers and 77% more in later layers. The gap survives matched corpus frequency and token fragmentation.
I really liked the angle of this paper. It does not just look at what the model says, but tries to understand what the model actually represents internally. That makes the work feel more original and deeper than a typical bias evaluation.
The most interesting insight for me is that Global South regions may be getting flattened inside the model itself, not just in the final answers. That is an important point because it suggests cultural fairness cannot be solved only by better prompting or nicer wording. I would also be interested in seeing this extended to countries with much higher internal diversity, such as India, where regional identity is shaped not only by cities but also by language, caste, religion, ethnicity, and local community. That kind of setting could make the paper’s core question even more powerful: whether models truly represent internal cultural diversity, or flatten complex societies into one broad national identity.
4 / 4 / 4
Good and important topic. The method is solid, but it needs stronger checks to prove the difference is really cultural representation and not another hidden factor.
No feedback. Meets all the criterias
Cite this work
@misc {
title={
(HckPrj) LLMs Flatten the Global South: Sub-Regional Representation Asymmetry
},
author={
Noah De Nicola
},
date={
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
