Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition
For researchers in computational second language acquisition, this provides a scalable, open-source model to study L1-to-L2 transfer with controlled contamination.
Dango, a 1.8B-parameter LLM, simulates Japanese-to-English second language acquisition by filtering L2 contamination from pretraining data and fine-tuning on L2 lessons, achieving human-like L2 production patterns that outperform baselines.
We introduce Dango, a 1.8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition (SLA). While previous studies have explored SLA in language models, they have predominantly relied on smaller or non-decoder models, limiting their ability to generate open-ended text and reducing their suitability as practical L2 simulators. We identify a key challenge when scaling models to this size: L2 contamination within the "monolingual" pretraining corpus used for L1 acquisition. To address this, we propose a filtering method to reduce premature exposure to English while preserving realistic, minimal exposure. We then fine-tune the model on LLM-generated L2-learning lessons to simulate the L2 acquisition process. Our evaluations confirm that Dango develops human-like L2 production patterns, outperforming both unfiltered and standard multilingual baselines. We release the model, data, and code to facilitate reproducible computational SLA research and learner-facing applications.