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Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study titled 'Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?' investigates the effectiveness of large language models (LLMs) versus smaller, task-specific models in the challenging task of converting Sanskrit poetry to prose. Sanskrit, a low-resource and morphologically rich language, presents unique challenges due to its free word order and strict metrical constraints. The research reveals that while LLMs are often viewed as universal solutions for various NLP tasks, this assumption does not hold for languages like Sanskrit. The ByT5-Sanskrit model, which underwent domain-specific fine-tuning, significantly outperformed all instruction-driven LLM approaches. This finding underscores the importance of specialized models in handling complex linguistic tasks, suggesting that advancements in NLP must consider the specific characteristics of diverse languages.
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