AI Diffusion in Low Resource Language Countries
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Abstract (arXiv)
Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 4 Nov 2025.
Builds on
4 of 20 references.
References · 4
- 2024Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluationcited by 181
- 2025FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Languagecited by 134
- 2024IrokoBench: A New Benchmark for African Languages in the Age of Large Language Modelscited by 43
- 2025Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languagescited by 4
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Citing papers per year: 2026: 2.
2 papers.