@inproceedings{kranti-vajjala-2026-metricalargs,
title = "{M}etrical{ARGS}: Studying Metrical Poetry with {LLM}s",
author = "Kranti, Chalamalasetti and
Vajjala, Sowmya",
editor = "Jha, Girish Nath and
Bali, Kalika and
L, Sobha and
Kumar, Devendr",
booktitle = "Proceedings of the 8th Workshop on {I}ndian Language Data: Resources and Evaluation",
month = may,
year = "2026",
address = "Palma, Mallorca, Spain",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.wildre-1.1/",
doi = "10.63317/4o3nvxw6qwso",
pages = "1--13",
abstract = "Many classical languages have well-studied traditions of poetic meter which enforce constraints on a poem in terms of syllable and phoneme patterns. Such advanced literary forms offer opportunities for probing deeper reasoning and language understanding in Large Language Models (LLMs) and their ability to follow strict pre-requisites and rules in generating text. In this paper, we introduce MetricalARGS, the first taxonomy of poetry-related NLP tasks designed to evaluate LLMs on metrical poetry across four dimensions: Analysis, Retrieval, Generation, and Support. We discuss how these tasks relate to existing NLP tasks, addressing questions around datasets and evaluation metrics. Taking the metrical poetry of Telugu language as our example, we illustrate how the taxonomy can be used with LLMs in practice through a quantitative and qualitative evaluation. MetricalARGS highlights the broader possibilities for understanding the capabilities and limitations of today{'}s LLMs through the lens of metrical poetry. We believe MetricalARGS can also serve as a reference taxonomy for studying and comparing metrical poetry across Indian languages as a starting point, and can be extended to other languages with established metrical poetry traditions."
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<abstract>Many classical languages have well-studied traditions of poetic meter which enforce constraints on a poem in terms of syllable and phoneme patterns. Such advanced literary forms offer opportunities for probing deeper reasoning and language understanding in Large Language Models (LLMs) and their ability to follow strict pre-requisites and rules in generating text. In this paper, we introduce MetricalARGS, the first taxonomy of poetry-related NLP tasks designed to evaluate LLMs on metrical poetry across four dimensions: Analysis, Retrieval, Generation, and Support. We discuss how these tasks relate to existing NLP tasks, addressing questions around datasets and evaluation metrics. Taking the metrical poetry of Telugu language as our example, we illustrate how the taxonomy can be used with LLMs in practice through a quantitative and qualitative evaluation. MetricalARGS highlights the broader possibilities for understanding the capabilities and limitations of today’s LLMs through the lens of metrical poetry. We believe MetricalARGS can also serve as a reference taxonomy for studying and comparing metrical poetry across Indian languages as a starting point, and can be extended to other languages with established metrical poetry traditions.</abstract>
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%0 Conference Proceedings
%T MetricalARGS: Studying Metrical Poetry with LLMs
%A Kranti, Chalamalasetti
%A Vajjala, Sowmya
%Y Jha, Girish Nath
%Y Bali, Kalika
%Y L, Sobha
%Y Kumar, Devendr
%S Proceedings of the 8th Workshop on Indian Language Data: Resources and Evaluation
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca, Spain
%F kranti-vajjala-2026-metricalargs
%X Many classical languages have well-studied traditions of poetic meter which enforce constraints on a poem in terms of syllable and phoneme patterns. Such advanced literary forms offer opportunities for probing deeper reasoning and language understanding in Large Language Models (LLMs) and their ability to follow strict pre-requisites and rules in generating text. In this paper, we introduce MetricalARGS, the first taxonomy of poetry-related NLP tasks designed to evaluate LLMs on metrical poetry across four dimensions: Analysis, Retrieval, Generation, and Support. We discuss how these tasks relate to existing NLP tasks, addressing questions around datasets and evaluation metrics. Taking the metrical poetry of Telugu language as our example, we illustrate how the taxonomy can be used with LLMs in practice through a quantitative and qualitative evaluation. MetricalARGS highlights the broader possibilities for understanding the capabilities and limitations of today’s LLMs through the lens of metrical poetry. We believe MetricalARGS can also serve as a reference taxonomy for studying and comparing metrical poetry across Indian languages as a starting point, and can be extended to other languages with established metrical poetry traditions.
%R 10.63317/4o3nvxw6qwso
%U https://aclanthology.org/2026.wildre-1.1/
%U https://doi.org/10.63317/4o3nvxw6qwso
%P 1-13
Markdown (Informal)
[MetricalARGS: Studying Metrical Poetry with LLMs](https://aclanthology.org/2026.wildre-1.1/) (Kranti & Vajjala, WILDRE 2026)
ACL
- Chalamalasetti Kranti and Sowmya Vajjala. 2026. MetricalARGS: Studying Metrical Poetry with LLMs. In Proceedings of the 8th Workshop on Indian Language Data: Resources and Evaluation, pages 1–13, Palma, Mallorca, Spain. ELRA Language Resources Association (ELRA).