# Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?

arXiv: https://arxiv.org/abs/2407.01119 · HTML: https://mlsanity.com/paper/2407.01119/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing](https://mlsanity.com/paper/2210.13669/) (2022)
- [The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing](https://mlsanity.com/paper/2403.12004/) (2024)
- [Divergent Creativity in Humans and Large Language Models](https://mlsanity.com/paper/2405.13012/) (2024)
- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers](https://mlsanity.com/paper/2108.00308/) (2021)

## Built on by (most cited first, 27 of 27)

- [WritingBench: A Comprehensive Benchmark for Generative Writing](https://mlsanity.com/paper/2503.05244/) (2025)
- [Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits](https://mlsanity.com/paper/2409.14509/) (2024)
- [AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation](https://mlsanity.com/paper/2504.07532/) (2025)
- [CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints](https://mlsanity.com/paper/2410.04197/) (2024)
- [Evaluating Creative Short Story Generation in Humans and Large Language Models](https://mlsanity.com/paper/2411.02316/) (2024)
- [Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs](https://mlsanity.com/paper/2409.11547/) (2024)
- [Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator](https://mlsanity.com/paper/2505.19236/) (2025)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writing](https://mlsanity.com/paper/2506.03310/) (2025)
- [ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities](https://mlsanity.com/paper/2506.02480/) (2025)
- [Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents](https://mlsanity.com/paper/2510.05188/) (2025)
- [AI Fiction in the Wild](https://mlsanity.com/paper/2606.22748/) (2026)
- [From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go](https://mlsanity.com/paper/2412.05445/) (2024)
- [GuessBench: Sensemaking Multimodal Creativity in the Wild](https://mlsanity.com/paper/2506.00814/) (2025)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling](https://mlsanity.com/paper/2604.09854/) (2026)
- [Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction](https://mlsanity.com/paper/2605.27878/) (2026)
- [Grandes modelos de lenguaje: de la predicci'on de palabras a la comprensi'on?](https://mlsanity.com/paper/2502.18205/) (2025)
- [Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity](https://mlsanity.com/paper/2503.00046/) (2025)
- [Local Differences, Global Lessons: Insights from Organisation Policies for International Legislation](https://mlsanity.com/paper/2503.05737/) (2025)
- [DSPO: Stable and Efficient Policy Optimization for Agentic Search and Reasoning](https://mlsanity.com/paper/2510.09255/) (2025)
- [Creative Convergence or Imitation? Genre-Specific Homogeneity in LLM-Generated Chinese Literature](https://mlsanity.com/paper/2603.14430/) (2026)
- [Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation](https://mlsanity.com/paper/2606.12599/) (2026)
- [Rethinking Indic AI from a Lens of Cultural Heritage Preservation](https://mlsanity.com/paper/2607.06544/) (2026)
- [CLIN: an Objective Framework for Evaluating Creativity in Short Persian Literary Text](https://mlsanity.com/paper/2608.30754/) (2026)
- [How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://mlsanity.com/paper/2609.02482/) (2026)
- [Evaluating LLM Creativity through Long-Tail Performance](https://mlsanity.com/paper/2610.12998/) (2026)

## Abstract (arXiv)

It has become routine to report research results where Large Language Models (LLMs) outperform average humans in a wide range of language-related tasks, and creative text writing is no exception. It seems natural, then, to raise the bid: Are LLMs ready to compete in creative writing skills with a top (rather than average) novelist? To provide an initial answer for this question, we have carried out a contest between Patricio Pron (an awarded novelist, considered one of the best of his generation) and GPT-4 (one of the top performing LLMs), in the spirit of AI-human duels such as DeepBlue vs Kasparov and AlphaGo vs Lee Sidol. We asked Pron and GPT-4 to provide thirty titles each, and then to write short stories for both their titles and their opponent's. Then, we prepared an evaluation rubric inspired by Boden's definition of creativity, and we collected 5,400 manual assessments provided by literature critics and scholars. The results of our experimentation indicate that LLMs are still far from challenging a top human creative writer, and that reaching such level of autonomous creative writing skills probably cannot be reached simply with larger language models.