From Plain Text to Poetic Form: Generating Metrically-Constrained Sanskrit Verses

• URL: http://arxiv.org/abs/2506.00815v1
• Date: Sun, 01 Jun 2025 03:35:46 GMT
• Title: From Plain Text to Poetic Form: Generating Metrically-Constrained Sanskrit Verses
• Authors: Manoj Balaji Jagadeeshan, Samarth Bhatia, Pretam Ray, Harshul Raj Surana, Akhil Rajeev P, Priya Mishra, Annarao Kulkarni, Ganesh Ramakrishnan, Prathosh AP, Pawan Goyal,
• Abstract summary: We introduce a dataset designed for translating English prose into structured Sanskrit verse.<n>We explore constrained decoding strategies and instruction-based fine-tuning tailored to metrical and semantic fidelity.
• Score: 22.08984009109879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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