DuDi: Dual-Signal Distillation Framework Improves Multilingual Capabilities of Small Language Models
Researchers have introduced DuDi, a dual-signal distillation framework designed to improve the multilingual performance of small language models (SLMs), particularly for Southeast Asian languages. SLMs typically suffer significant capability degradation at sub-billion parameter scales in multilingual settings, a gap DuDi aims to close by combining sequence-level and token-level learning signals with a cross-lingual verbalizer. The work addresses a meaningful gap in NLP accessibility for underrepresented language communities in Southeast Asia.
DuDi (Dual-Signal Distillation with Cross-Lingual Verbalizer) is a new knowledge distillation framework targeting the well-documented weakness of small language models in handling multilingual tasks, especially for Southeast Asian (SEA) languages. The framework combines an online sequence-level signal with both off-policy and on-policy token-level signals, aiming to provide richer and more complementary supervision during the teacher-to-student knowledge transfer process. A cross-lingual verbalizer is additionally employed to refine teacher feedback and improve transferability across languages. Experiments conducted on the SEA-HELM benchmark across multiple model families, scales, and teacher-student configurations show DuDi consistently outperforming competitive distillation baselines. Ablation studies confirm that each component — sequence-level optimization, token-level supervision, and cross-lingual verbalization — contributes independently and complementarily to overall performance gains.
What's missing
The paper does not report absolute performance numbers or effect sizes in the abstract, making it difficult to assess the practical magnitude of improvements over baselines. Real-world deployment performance on low-resource SEA languages beyond benchmark conditions remains an open question.
What different sources said
- arXiv cs.CLCenter
DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer
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