Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

ArXi:2512.04844v2 Announce Type: replace-cross Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled target language data. We