Poetry generation has been a challenging task in the field of Natural Language Processing, as it requires the model to understand the nuances of language, sentiment, and style. In this paper, we propose using Large Language Models to generate Vietnamese poems from natural language prompts, thereby facilitating an intuitive process with enhanced content control. Our most efficacious model, the GPT-3 Babbage variant, achieves a custom evaluation score of 0.8, specifically tailored to the "luc bat" genre of Vietnamese poetry. Furthermore, we also explore the idea of paraphrasing poems into normal text prompts and yield a relatively high score of 0.718 in the "luc bat" genre. This experiment presents the potential for cross-Language poem-to-poem translation with translated poems as the inputs while concurrently maintaining complete control over the generated content.

翻译：诗歌生成一直是自然语言处理领域中的一项具有挑战性的任务，因为它要求模型理解语言、情感和风格的细微差别。本文提出使用大型语言模型从自然语言提示生成越南诗歌，从而促进直观的流程并增强内容控制。我们最有效的模型——GPT-3 Babbage变体——在越南诗歌“六八体”这一特定体裁中取得了定制的评估得分0.8。此外，我们还探索了将诗歌改写为普通文本提示的想法，并在“六八体”体裁中获得了相对较高的0.718得分。这一实验展现了跨语言诗歌间翻译的潜力，即使用翻译后的诗歌作为输入，同时保持对生成内容的完全控制。