  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 of various genres 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 0.781 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.
