让AI根据指令生成阿拉伯语诗歌，支持标准语与方言。
Instruction-Guided Poetry Generation in Arabic and Its Dialects
- 构建多语种阿拉伯语诗歌指令数据集，支持风格与押韵控制。
- 微调后模型在自动评估与母语者评测中均表现优秀。
- 适合对阿拉伯语文化创作或语言模型可控生成感兴趣的读者。
诗歌是阿拉伯语使用者重要的艺术形式和文化表达载体。尽管现代阿拉伯语使用者仍重视诗歌，但现有大型语言模型（LLMs）对阿拉伯语诗歌的研究主要集中在分析任务，如韵律模式与标题预测。本文则聚焦于诗歌创作的实用需求，提出一种大规模、精心构建的基于指令的阿拉伯语诗歌数据集，涵盖现代标准阿拉伯语（MSA）及多种方言。该数据集支持根据预设条件（如风格、押韵）进行诗歌创作、修改与续写，并可完成诗歌分析。实验表明，基于该数据集微调的LLM能有效生成符合用户要求的诗歌，通过自动化指标与母语者人工评估验证。数据与代码已公开于https://github.com/mbzuai-nlp/instructpoet-ar。
原文摘要 · Abstract (English)
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
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