Neural metrics for machine translation (MT) evaluation have become increasingly prominent due to their superior correlation with human judgments compared to traditional lexical metrics. Researchers have therefore utilized neural metrics through quality-informed decoding strategies, achieving better results than likelihood-based methods. With the rise of Large Language Models (LLMs), preference-based alignment techniques have gained attention for their potential to enhance translation quality by optimizing model weights directly on preferences induced by quality estimators. This study focuses on Contrastive Preference Optimization (CPO) and conducts extensive experiments to evaluate the impact of preference-based alignment on translation quality. Our findings indicate that while CPO consistently outperforms Supervised Fine-Tuning (SFT) on high-quality data with regard to the alignment metric, it may lead to instability across downstream evaluation metrics, particularly between neural and lexical ones. Additionally, we demonstrate that relying solely on the base model for generating candidate translations achieves performance comparable to using multiple external systems, while ensuring better consistency across downstream metrics.

本文探讨在机器翻译中，通过偏好对齐技术(CPO)提升翻译质量的有效性，解决了偏好对齐方法可能导致的评估不稳定性问题。研究发现，虽然CPO在高质量数据中优于监督微调(SFT)，但在不同评估指标之间的稳定性存在挑战，同时仅依赖基础模型生成的候选翻译在一致性和性能上表现良好。

增强基于大语言模型的翻译时，偏好对齐是否总是最佳选择？实证分析