Don’t Throw Away Data: Better Sequence Knowledge Distillation

Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn

Keywords: Knowledge distillation, machine translation, large language model

TL;DR: Use multiple minimum Bayes risk decoding results for knowledge distillation in machine translation tasks with LLMs

Abstract: A critical component in knowledge distillation is the means of coupling the teacher and student. The predominant sequence knowledge distillation method involves supervised learning of the student against teacher-decoded outputs, and is exemplified by the current state of the art, which incorporates minimum Bayes risk (MBR) decoding. In this paper we seek to integrate MBR more tightly in distillation training, specifically by using several high scoring MBR translations, rather than a single selected sequence, thus capturing a rich diversity of teacher outputs. Our experiments on English to German and English to Japanese translation show consistent improvements over strong baseline methods for both tasks and with varying model sizes. Additionally, we conduct a detailed analysis focusing on data efficiency and capacity curse aspects to elucidate MBR-n and explore its further potential.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 8776