Bucket-Level MOO reformulates multilingual fine-tuning as localized multi-objective optimization and proves it enforces a tighter Pareto stationarity condition while improving cross-lingual performance on four LLMs.
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages
3 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
COPSD improves mathematical reasoning in low-resource languages by having LLMs self-distill from their own high-resource English behavior via token-level divergence on rollouts with privileged crosslingual context.
DuDi is a dual-signal distillation method with cross-lingual verbalizer that improves multilingual SLM performance on SEA languages and outperforms baselines on SEA-HELM.
citing papers explorer
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Multilingual Fine-Tuning via Localized Gradient Conflict Resolution
Bucket-Level MOO reformulates multilingual fine-tuning as localized multi-objective optimization and proves it enforces a tighter Pareto stationarity condition while improving cross-lingual performance on four LLMs.
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Crosslingual On-Policy Self-Distillation for Multilingual Reasoning
COPSD improves mathematical reasoning in low-resource languages by having LLMs self-distill from their own high-resource English behavior via token-level divergence on rollouts with privileged crosslingual context.
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DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer
DuDi is a dual-signal distillation method with cross-lingual verbalizer that improves multilingual SLM performance on SEA languages and outperforms baselines on SEA-HELM.