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    <abstract>Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by enabling controlled comparisons across language combinations and learning conditions. Recent work explores this direction by training bilingual language models under developmentally plausible constraints. However, human and model learners still diverge in fundamental ways, with one major difference being input modality: children learn primarily from spoken input, whereas language models are typically trained on orthographic text. To reduce this gap, researchers have trained models on phonemic representations of speech. In this work, we combine these research directions to train bilingual BabyLMs with phonemic input. We keep English fixed as the L2 and vary the L1 across German, Swedish, Persian, and Basque, selected to represent contrasting combinations of syntactic and phoneme-inventory distance from English. Our results show stronger L1-related variation in grammatical learning trajectories under phonemic than orthographic input, while early lexical differences align with phoneme-inventory similarity.</abstract>
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%0 Conference Proceedings
%T Cross-Linguistic Effects in Bilingual Phoneme BabyLMs
%A Theodoropoulos, Nikitas
%A Lymperaiou, Maria
%A Filandrianos, Giorgos
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F theodoropoulos-etal-2026-cross
%X Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by enabling controlled comparisons across language combinations and learning conditions. Recent work explores this direction by training bilingual language models under developmentally plausible constraints. However, human and model learners still diverge in fundamental ways, with one major difference being input modality: children learn primarily from spoken input, whereas language models are typically trained on orthographic text. To reduce this gap, researchers have trained models on phonemic representations of speech. In this work, we combine these research directions to train bilingual BabyLMs with phonemic input. We keep English fixed as the L2 and vary the L1 across German, Swedish, Persian, and Basque, selected to represent contrasting combinations of syntactic and phoneme-inventory distance from English. Our results show stronger L1-related variation in grammatical learning trajectories under phonemic than orthographic input, while early lexical differences align with phoneme-inventory similarity.
%U https://aclanthology.org/2026.babylm-main.26/
%P 320-332

Markdown (Informal)

[Cross-Linguistic Effects in Bilingual Phoneme BabyLMs](https://aclanthology.org/2026.babylm-main.26/) (Theodoropoulos et al., BabyLM 2026)

• Cross-Linguistic Effects in Bilingual Phoneme BabyLMs (Theodoropoulos et al., BabyLM 2026)

ACL

• Nikitas Theodoropoulos, Maria Lymperaiou, and Giorgos Filandrianos. 2026. Cross-Linguistic Effects in Bilingual Phoneme BabyLMs. In Proceedings of the Second BabyLM Workshop, pages 320–332, Budapest, Hungary. Association for Computational Linguistics.