Pretrained Multilingual Transformers Reveal Quantitative Distance Between Human Languages
Understanding the distance between human languages is central to linguistics, anthropology, and tracing human evolutionary history. Yet, while linguistics has long provided rich qualitative accounts of cross-linguistic variation, a unified and scalable quantitative approach to measuring language distance remains lacking. In this paper, we introduce a method that leverages pretrained multilingual language models as systematic instruments for linguistic measurement. Specifically, we show that the spontaneously emerged attention mechanisms of these models provide a robust, tokenization-agnostic measure of cross-linguistic distance, termed Attention Transport Distance (ATD). By treating atten...
| Authors | Yue Zhao, Jiatao Gu, Paloma Jeretič et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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