Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
This work addresses translation quality instability for researchers and practitioners using LLMs, highlighting incremental insights into alignment methods.
The study investigated whether preference-based alignment (Contrastive Preference Optimization) consistently improves LLM-based translation quality, finding that while it outperforms Supervised Fine-Tuning on alignment metrics, it can cause instability across downstream evaluation metrics like neural and lexical ones.
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.