From Plain Text to Poetic Form: Generating Metrically-Constrained Sanskrit Verses
This work addresses the challenge of structured poetic generation for a low-resource, morphologically rich language, representing an incremental advancement in adapting LLMs to specific linguistic and cultural contexts.
The paper tackled generating metrically-constrained Sanskrit verses from English prose, achieving over 99% accuracy in producing syntactically valid poetic forms and improved alignment with source meaning through instruction-based fine-tuning.
Recent advances in large language models (LLMs) have significantly improved natural language generation, including creative tasks like poetry composition. However, most progress remains concentrated in high-resource languages. This raises an important question: Can LLMs be adapted for structured poetic generation in a low-resource, morphologically rich language such as Sanskrit? In this work, we introduce a dataset designed for translating English prose into structured Sanskrit verse, with strict adherence to classical metrical patterns, particularly the Anushtub meter. We evaluate a range of generative models-both open-source and proprietary-under multiple settings. Specifically, we explore constrained decoding strategies and instruction-based fine-tuning tailored to metrical and semantic fidelity. Our decoding approach achieves over 99% accuracy in producing syntactically valid poetic forms, substantially outperforming general-purpose models in meter conformity. Meanwhile, instruction-tuned variants show improved alignment with source meaning and poetic style, as supported by human assessments, albeit with marginal trade-offs in metrical precision.