Leveraging Generative Pre-trained Language Models for Advanced Unsupervised Neural Machine Translation

Abstract: This paper presents a novel methodology for advancing unsupervised neural machine translation (NMT) systems using large, pre-trained language models, notably focusing on GPT-3. The proposed approach involves a three-step process: few-shot amplification, distillation, and backtranslation. Through experiments on the WMT14 English-French benchmark, the methodology achieves state-of-the-art results, demonstrating its effectiveness and versatility. Challenges such as few-shot prompting and model scalability are addressed, showcasing the robustness of the approach. Experimental results across different model sizes and configurations highlight its adaptability. The findings suggest that leveraging generative pre-trained language models offers promising avenues for enhancing unsupervised NMT systems. This methodology not only advances the state-of-the-art in machine translation but also lays the foundation for broader applications in sequence-to-sequence tasks. Further exploration of this approach could lead to significant advancements in the field of natural language processing.

Keywords: Unsupervised Neural Machine Translation, Generative Pre-trained Language Models, Few-shot Amplification, Distillation, Backtranslation, Zero-shot Translation, Experimental Evaluation, GPT-3.