Accurate Knowledge Distillation with n-best Reranking

• URL: http://arxiv.org/abs/2305.12057v4
• Date: Wed, 12 Jun 2024 18:28:01 GMT
• Title: Accurate Knowledge Distillation with n-best Reranking
• Authors: Hendra Setiawan,
• Abstract summary: We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) We 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. Our results demonstrate that utilizing pseudo-labels generated by our n-best reranker leads to a significantly more accurate student model.
• Score: 2.9526110883017433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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