SSU mitigates catastrophic forgetting in low-resource LLM target-language adaptation by scoring and column-wise freezing source-critical parameters, reducing source degradation to ~3% versus ~20% for full fine-tuning while matching target performance.
Cendol: Open instruction-tuned generative large language models for I ndonesian languages
4 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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IndoSafety, a culturally grounded safety benchmark for five Indonesian language varieties, shows unsafe response rates up to 40% in regional models and demonstrates that safety tuning on formal Indonesian transfers to local languages.
Entropy2Vec turns the cross-lingual surprise of monolingual language models into dense language embeddings that resemble typological families and match curated vectors in downstream tasks.
Lius improves LLM translation for Kupang Malay by 4-13 points over baselines via continual instruction tuning with dictionary-derived instructions.
citing papers explorer
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Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
SSU mitigates catastrophic forgetting in low-resource LLM target-language adaptation by scoring and column-wise freezing source-critical parameters, reducing source degradation to ~3% versus ~20% for full fine-tuning while matching target performance.
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IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages
IndoSafety, a culturally grounded safety benchmark for five Indonesian language varieties, shows unsafe response rates up to 40% in regional models and demonstrates that safety tuning on formal Indonesian transfers to local languages.
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Entropy2Vec: Crosslingual Language Modeling Entropy as End-to-End Learnable Language Representations
Entropy2Vec turns the cross-lingual surprise of monolingual language models into dense language embeddings that resemble typological families and match curated vectors in downstream tasks.
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Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay
Lius improves LLM translation for Kupang Malay by 4-13 points over baselines via continual instruction tuning with dictionary-derived instructions.