Accurate Knowledge Distillation with n-best Reranking

Hendra Setiawan

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arXiv · 2305.12057

Accurate Knowledge Distillation with n-best Reranking

Hendra Setiawan

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Abstract

We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model's training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including some publicly-available large language models, to pick the highest-quality hypotheses as labels. The effectiveness of our proposal is validated through experiments on the WMT'21 German-English and Chinese-English translation tasks. Our results demonstrate that utilizing pseudo-labels generated by our n-best reranker leads to a significantly more accurate student model. In fact, our best student model achieves comparable accuracy to a large translation model from (Tran et al., 2021) with 4.7 billion parameters, while having two orders of magnitude fewer parameters.

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Hendra Setiawan. 2023-05-20. Accurate Knowledge Distillation with n-best Reranking. https://arxiv.org/abs/2305.12057

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