One Form to Transfer Them All: Pretraining Multilingual Language Models Beyond Native Orthography
Multilingual language models transfer knowledge across languages through shared subword vocabulary, a mechanism that breaks down when related languages use different writing systems. Prior work addresses this via script equalization (romanization or IPA transcription), but direct comparisons are rare; the focus has been on encoder-only models, with most work adapting existing pretrained models. We systematically compare different input representations in autoregressive multilingual pretraining, comparing orthographic text, IPA, and romanization in a controlled setup across three scales (467M, 709M, and 1.03B) on eight languages in four typologically motivated pairs. Across a wide range of...
| Authors | Muge Zhang, Aaron Jencks, Krishna Badikela et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 August 2026 |
| Last updated at source | 8 September 2026 |
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