MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods

TL;DR

This paper introduces finetuning methods that incorporate the benefits of expensive decoding techniques like MBR and QE during training, enabling high-quality NLG outputs with efficient decoding at inference time.

Contribution

It proposes MBR and QE finetuning approaches that distill decoding quality improvements into training, outperforming base models and even human references when using external teachers.

Findings

Finetuning with MBR and QE surpasses base model performance.

External LLM teachers lead to better results than human references.

Methods achieve high-quality outputs with efficient decoding.

Abstract

Recent research in decoding methods for Natural Language Generation (NLG) tasks has shown that MAP decoding is not optimal, because model probabilities do not always align with human preferences. Stronger decoding methods, including Quality Estimation (QE) reranking and Minimum Bayes' Risk (MBR) decoding, have since been proposed to mitigate the model-perplexity-vs-quality mismatch. While these decoding methods achieve state-of-the-art performance, they are prohibitively expensive to compute. In this work, we propose MBR finetuning and QE finetuning which distill the quality gains from these decoding methods at training time, while using an efficient decoding algorithm at inference time. Using the canonical NLG task of Neural Machine Translation (NMT), we show that even with self-training, these finetuning methods significantly outperform the base model. Moreover, when using an external LLM as a teacher model, these finetuning methods outperform finetuning on human-generated references. These findings suggest new ways to leverage monolingual data to achieve improvements in model quality that are on par with, or even exceed, improvements from human-curated data, while maintaining maximum efficiency during decoding.