Instruction-Guided Poetry Generation in Arabic and Its Dialects

TL;DR

This paper introduces a large-scale, instruction-based dataset for Arabic poetry generation and demonstrates that fine-tuned LLMs can effectively create poetry aligned with user specifications.

Contribution

It provides the first comprehensive dataset and methodology for controllable Arabic poetry generation using LLMs, covering Modern Standard Arabic and dialects.

Findings

Fine-tuned models produce poetry matching user criteria.

Automated metrics and human evaluations confirm quality.

Dataset enables tasks like writing, revising, and analyzing Arabic poetry.

Abstract

Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic speakers continue to value poetry, existing research on Arabic poetry within Large Language Models (LLMs) has primarily focused on analysis tasks such as interpretation or metadata prediction, e.g., rhyme schemes and titles. In contrast, our work addresses the practical aspect of poetry creation in Arabic by introducing controllable generation capabilities to assist users in writing poetry. Specifically, we present a large-scale, carefully curated instruction-based dataset in Modern Standard Arabic (MSA) and various Arabic dialects. This dataset enables tasks such as writing, revising, and continuing poems based on predefined criteria, including style and rhyme, as well as performing poetry analysis. Our experiments show that fine-tuning LLMs on this dataset yields models that can effectively generate poetry that is aligned with user requirements, based on both automated metrics and human evaluation with native Arabic speakers. The data and the code are available at https://github.com/mbzuai-nlp/instructpoet-ar