Reinforcement Learning from Human Feedback (RLHF) and derivative techniques like Direct Preference Optimization (DPO) are task-alignment algorithms used to repurpose general, foundational models for specific tasks. We show that applying task-alignment to neural machine translation (NMT) addresses an existing task--data mismatch in NMT, leading to improvements across all languages of a multilingual model, even when task-alignment is only applied to a subset of those languages. We do so by introducing Direct Quality Optimization (DQO), a variant of DPO leveraging a pre-trained translation quality estimation model as a proxy for human preferences, and verify the improvements with both automatic metrics and human evaluation.
@article{arxiv.2409.17673,
title = {Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization},
author = {Kaden Uhlig and Joern Wuebker and Raphael Reinauer and John DeNero},
journal= {arXiv preprint arXiv:2409.17673},
year = {2025}
}