High-quality automated poetry generation systems are currently only available for a small subset of languages. We introduce a new model for generating poetry in Czech language, based on fine-tuning a pre-trained Large Language Model. We demonstrate that guiding the generation process by explicitly specifying strophe parameters within the poem text strongly improves the effectiveness of the model. We also find that appropriate tokenization is crucial, showing that tokenization methods based on syllables or individual characters instead of subwords prove superior in generating poetic strophes. We further enhance the results by introducing \textit{Forced~generation}, adding explicit specifications of meter and verse parameters at inference time based on the already generated text. We evaluate a range of setups, showing that our proposed approach achieves high accuracies in rhyming and metric aspects of formal quality of the generated poems.

我们引入了一种新的模型，用于在捷克语中生成诗歌，该模型在预训练的大型语言模型的基础上进行了微调。我们展示了在诗歌文本中明确指定诗节参数来引导生成过程可以大大提高模型的效果。我们还发现适当的分词非常关键，基于音节或单个字符而不是子词的分词方法在生成诗歌节的方面表现更好。我们通过引入“强制生成”技术进一步改进了结果，根据已生成的文本在推理时增加了韵律和诗句参数的明确规定。我们评估了一系列设置，表明我们提出的方法在生成的诗歌的韵律和度量方面实现了高准确性的形式质量。

捷克詩人的 GPT：利用語言模型生成捷克詩歌詩節