Generation of Russian Poetry of Different Genres and Styles Using Neural Networks with Character-Level Tokenization
Ilya Koziev, Alena Fenogenova · 2025
Automatic poetry generation is an immensely complex task, even for the most advanced Large Language Models (LLMs) that requires a profound understanding of intelligence, world and linguistic knowledge, and a touch of creativity.This paper investigates the use of LLMs in generating Russian syllabo-tonic poetry of various genres and styles.The study explores a character-level tokenization architectures and demonstrates how a language model can be pretrained and finetuned to generate poetry requiring knowledge of a language's phonetics.Additionally, the paper assesses the quality of the generated poetry and the effectiveness of the approach in producing different genres and styles.The study's main contribution is the introduction of two end-to-end architectures for syllabo-tonic Russian poetry: pretrained models, a comparative analysis of the approaches, and poetry evaluation metrics.