# Cross-Linguistic Effects in Bilingual Phoneme BabyLMs

arXiv: https://arxiv.org/abs/2609.37121 · HTML: https://mlsanity.com/paper/2609.37121/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner](https://mlsanity.com/paper/1607.08723/) (2016)
- [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://mlsanity.com/paper/2306.01506/) (2023)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [A Bit of a Problem: Measurement Disparities in Dataset Sizes Across Languages](https://mlsanity.com/paper/2403.00686/) (2024)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT](https://mlsanity.com/paper/2305.19589/) (2023)
- [From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes](https://mlsanity.com/paper/2410.22906/) (2024)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [On the Acquisition of Shared Grammatical Representations in Bilingual Language Models](https://mlsanity.com/paper/2503.03962/) (2025)
- [IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling](https://mlsanity.com/paper/2504.03036/) (2025)
- [BAMBINO-LM: (Bilingual-)Human-Inspired Continual Pretraining of BabyLM](https://mlsanity.com/paper/2406.11418/) (2024)
- [Small Language Models Also Work With Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas](https://mlsanity.com/paper/2410.01487/) (2024)
- [BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings](https://mlsanity.com/paper/2509.15001/) (2025)
- [BabyLM's First Words: Word Segmentation as a Phonological Probing Task](https://mlsanity.com/paper/2504.03338/) (2025)

## Abstract (arXiv)

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.