# Introducing Aspects of Creativity in Automatic Poetry Generation

arXiv: https://arxiv.org/abs/2002.02511 · HTML: https://mlsanity.com/paper/2002.02511/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [From Role-Play to Drama-Interaction: An LLM Solution](https://mlsanity.com/paper/2405.14231/) (2024)
- [LLM-based multi-agent poetry generation in non-cooperative environments](https://mlsanity.com/paper/2409.03659/) (2024)
- [Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator](https://mlsanity.com/paper/2505.19236/) (2025)
- [Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry](https://mlsanity.com/paper/2509.01620/) (2025)
- [Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry](https://mlsanity.com/paper/2109.14934/) (2021)
- [Can AI Write Classical Chinese Poetry like Humans? An Empirical Study Inspired by Turing Test](https://mlsanity.com/paper/2401.04952/) (2024)
- [GPoeT-2: A GPT-2 Based Poem Generator](https://mlsanity.com/paper/2205.08847/) (2022)
- [Creative Data Generation: A Review Focusing on Text and Poetry](https://mlsanity.com/paper/2305.08493/) (2023)
- [Chandomitra: Towards Generating Structured Sanskrit Poetry from Natural Language Inputs](https://mlsanity.com/paper/2506.00815/) (2025)
- [TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques](https://mlsanity.com/paper/2312.02125/) (2023)
- [A Survey of Pun Generation: Datasets, Evaluations and Methodologies](https://mlsanity.com/paper/2507.04793/) (2025)
- [POEMetric: The Last Stanza of Humanity](https://mlsanity.com/paper/2604.03695/) (2026)
- [Computational Storytelling and Emotions: A Survey](https://mlsanity.com/paper/2205.10967/) (2022)
- [Urdu Poetry Generated by Using Deep Learning Techniques](https://mlsanity.com/paper/2309.14233/) (2023)

## Abstract (arXiv)

Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams---called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.