Why Pretraining Fails to Share Cross-Lingual Knowledge
Large Language Models (LLMs) have made remarkable progress in the processing and modeling of many languages. Yet, unlike human multilinguals, they exhibit surprisingly limited cross-lingual knowledge transfer. While this limitation is well documented, its origins during multilingual training remain unclear. We pretrain 360M- and 7B-parameter LLMs and show that poor cross-lingual knowledge generalization emerges during pretraining and persists under standard interventions. To isolate its cause, we employ a controlled bilingual pretraining setting using two copies of the same language, sharing identical text and token segmentation, but mapped to disjoint token spaces. We find that disjoint ...
| Authors | Adam Gaber, Uriel Dolev, Elisabeth Fittschen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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