DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer

Abstract:Small language models (SLMs) are efficient and scalable, but their multilingual capabilities degrade severely at sub-billion scales, especially for Southeast Asian (SEA) languages. We introduce DuDi, a dual-signal multilingual distillation framework that combines an online sequence-level signal with off-policy and on-policy token-level signals. DuDi further uses a cross-lingual verbalizer to refine teacher feedback and improve teacher-student transferability in multilingual settings. Experiments on SEA-HELM across multiple model families, scales, and teacher-student settings show that DuDi consistently outperforms competitive distillation baselines. Ablations and analyses confirm that sequence-level optimization, token-level supervision, and cross-lingual verbalization provide complementary and transferable learning signals for multilingual SLMs.

Computation and Language (cs.CL)

arXiv:2606.04694 [cs.CL]

(or arXiv:2606.04694v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.04694 Focus to learn more arXiv-issued DOI via DataCite

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