# MetricalARGS: A Taxonomy for Studying Metrical Poetry with LLMs

arXiv: https://arxiv.org/abs/2510.08188 · HTML: https://mlsanity.com/paper/2510.08188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme](https://mlsanity.com/paper/1807.03491/) (2018)
- [ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models](https://mlsanity.com/paper/2212.10474/) (2022)
- [PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation](https://mlsanity.com/paper/2205.12206/) (2022)
- [CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM](https://mlsanity.com/paper/2401.03512/) (2024)
- [Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets](https://mlsanity.com/paper/2406.18906/) (2024)
- [Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs](https://mlsanity.com/paper/2505.18152/) (2025)
- [Don't Go Far Off: An Empirical Study on Neural Poetry Translation](https://mlsanity.com/paper/2109.02972/) (2021)
- [GEE! Grammar Error Explanation with Large Language Models](https://mlsanity.com/paper/2311.09517/) (2023)
- [What is the Best Way for ChatGPT to Translate Poetry?](https://mlsanity.com/paper/2406.03450/) (2024)
- [Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs](https://mlsanity.com/paper/2506.00815/) (2025)
- [A Comparison of Feature-Based and Neural Scansion of Poetry](https://mlsanity.com/paper/1711.00938/) (2017)
- [Chandojnanam: A Sanskrit Meter Identification and Utilization System](https://mlsanity.com/paper/2209.14924/) (2022)
- [PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs](https://mlsanity.com/paper/2508.02515/) (2025)
- [Aesthetics of Sanskrit Poetry from the Perspective of Computational Linguistics: A Case Study Analysis on Siksastaka](https://mlsanity.com/paper/2308.07081/) (2023)

## Built on by (most cited first, 1 of 1)

- [Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry](https://mlsanity.com/paper/2609.19154/) (2026)

## Abstract (arXiv)

Prior NLP work studying poetry has focused primarily on automatic poem generation and summarization. Many 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 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 Telugu as our example language, we illustrate how the taxonomy can be used in practice. MetricalARGS highlights the broader possibilities for understanding the capabilities and limitations of today's LLMs through the lens of metrical poetry.