Papers › f-Divergence Minimization for Sequence-Level Knowledge Distillation

f-Divergence Minimization for Sequence-Level Knowledge Distillation

27 Jul 2023arXiv:2307.15190archive 2025-07-28

Yuqiao Wen, Zichao Li, Wenyu Du, Lili Mou

Knowledge distillation (KD) is the process of transferring knowledge from a large model to a small one. It has gained increasing attention in the natural language processing community, driven by the demands of compressing ever-growing language models. In this work, we propose an f-DISTILL framework, which formulates sequence-level knowledge distillation as minimizing a generalized f-divergence function. We propose four distilling variants under our framework and show that existing SeqKD and ENGINE approaches are approximations of our f-DISTILL methods. We further derive step-wise decomposition for our f-DISTILL, reducing intractable sequence-level divergence to word-level losses that can be computed in a tractable manner. Experiments across four datasets show that our methods outperform existing KD approaches, and that our symmetric distilling losses can better force the student to learn from the teacher distribution.

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