arXiv:2606.19170v1 Announce Type: new Abstract: 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 acquisitio
The development of Dango reflects the accelerating push to apply specialized large language models to complex cognitive tasks like second language acquisition, moving beyond general-purpose applications.
This research provides a more robust tool for studying human language learning, offering insights into L1-L2 transfer that could inform more effective language education and AI systems.
The introduction of L1-only LLMs like Dango allows for more controlled experimental conditions in SLA research, potentially refining our understanding of language acquisition mechanisms in both humans and machines.
- · AI researchers
- · Linguists
- · EdTech companies
- · Language learning platforms
- · Traditional language acquisition research methods
- · Less specialized AI models for SLA
More sophisticated and scientifically grounded AI tools emerge for language education and analysis.
Understanding of L1-L2 transfer mechanisms could lead to more efficient human language learning strategies and curriculum development.
The methodology could be extended to study other cognitive biases or learning processes in AI, bridging neuroscience and AI development.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL