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arXiv Computation and Language

Bilingual Language Models Capture Cross-Linguistic Influence in Second-Language Reading

The study examines whether bilingual language models can reflect the influence of a reader’s first language (L1) on second-language (L2) reading. By comparing bilingual models trained on an L1 that matches the readers’ backgrounds to monolingual English models and bilingual models trained on other L1s, the authors find that matching L1 improves predictions of L2 reading times, especially in late-stage eye-tracking measures and among intermediate-proficiency readers. The effect is modulated by the overlap between L1 and L2 scripts, indicating that bilingual models capture cross-linguistic influence in L2 reading behavior.

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