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.
Mitigating Catastrophic Forgetting in Language Transfer via Model Merging
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2representative citing papers
Jupiter-N is a post-trained version of Nemotron 3 Super that reports gains on Welsh benchmarks, terminal agent tasks, and instruction following while retaining base capabilities, released openly as a template for sovereign cultural AI adaptation.
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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Jupiter-N Technical Report
Jupiter-N is a post-trained version of Nemotron 3 Super that reports gains on Welsh benchmarks, terminal agent tasks, and instruction following while retaining base capabilities, released openly as a template for sovereign cultural AI adaptation.