  Recent advances in language models (LMs), have demonstrated significant
efficacy in tasks related to the arts and humanities. While LMs have exhibited
exceptional performance across a wide range of natural language processing
tasks, there are notable challenges associated with their utilization on small
datasets and their ability to replicate more creative human capacities. In this
study, we aim to address these challenges by training a Persian classical
poetry generation model using a transformer architecture on a specialized
dataset with no pretraining. Additionally, we propose a novel decoding method
to enhance coherence and meaningfulness in the generated poetry, effectively
managing the tradeoff between diversity and quality. Furthermore, the results
of our training approach and the proposed decoding method are evaluated through
comprehensive set of automatic and human evaluations and showed its superior
capability to generate coherent and meaningful poetry in compare to other
decoding methods and an existing Persian large language model (LLM).
