Contrastive Preference Optimization
Contrastive Preference Optimization for enhancing machine translation performance
CommonProductProductivityMachine TranslationContrastive Preference Optimization
Contrastive Preference Optimization is an innovative method for machine translation that trains models to avoid generating merely adequate but imperfect translations, resulting in a significant performance boost for the ALMA model. This method achieves or surpasses the performance of WMT competition winners and GPT-4 on WMT'21, WMT'22, and WMT'23 test datasets.
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