[2603.29552] Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models

Computer Science > Computation and Language arXiv:2603.29552 (cs) [Submitted on 31 Mar 2026] Title:Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models Authors:Linda Zeng, Steven Y. Feng, Michael C. Frank View a PDF of the paper titled Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models, by Linda Zeng and 2 other authors View PDF HTML (experimental) Abstract:Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, but it is surprisingly difficult to get definitive answers because children cannot be randomly assigned to be multilingual and data are typically not matched between languages. We use language model training as a method for simulating a variety of highly controlled exposure conditions, and create matched 100M-word mono- and bilingual datasets using synthetic data and machine translation. We train GPT-2 models on monolingual and bilingual data organized to reflect a range of exposure regimes, and evaluate their performance on perplexity, grammaticality, and semantic knowledge. Across model scales and measures, bilingual models perform similarly to monolingual models...