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Recent advances in language models (LMs), have demonstrated significantefficacy in tasks related to the arts and humanities. While LMs have exhibitedexceptional performance across a wide range of natural language processingtasks, there are notable challenges associated with their utilization on smalldatasets and their ability to replicate more creative human capacities. In thisstudy, we aim to address these challenges by training a Persian classicalpoetry generation model using a transformer architecture on a specializeddataset with no pretraining. Additionally, we propose a novel decoding methodto enhance coherence and meaningfulness in the generated poetry, effectivelymanaging the tradeoff between diversity and quality. Furthermore, the resultsof our training approach and the proposed decoding method are evaluated throughcomprehensive set of automatic and human evaluations and showed its superiorcapability to generate coherent and meaningful poetry in compare to otherdecoding methods and an existing Persian large language model (LLM).

TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques