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EMNLP Findings2023

A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing

Carlos Gómez-Rodríguez, Paul Williams

Abstract

We evaluate a range of recent LLMs on English creative writing, a challenging and complex task that requires imagination, coherence, and style. We use a difficult, open-ended scenario chosen to avoid training data reuse: an epic narration of a single combat between Ignatius J. Reilly, the protagonist of the Pulitzer Prize-winning novel A Confederacy of Dunces (1980), and a pterodactyl, a prehistoric flying reptile. We ask several LLMs and humans to write such a story and conduct a human evalution involving various criteria such as fluency, coherence, originality, humor, and style. Our results show that some state-of-the-art commercial LLMs match or slightly outperform our writers in most dimensions; whereas opensource LLMs lag behind. Humans retain an edge in creativity, while humor shows a binary divide between LLMs that can handle it comparably to humans and those that fail at it. We discuss the implications and limitations of our study and suggest directions for future research.

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Cited by top-tier papers21

  1. Creation-Mmbench: Assessing Context-Aware Creative Intelligence in MllmsXinyu Fang, Zhijian Chen, Kai Lan, Lixin Ma et al.ICCV 2025 · 23 citations
  2. RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint DataZhengkang Guo, Wenhao Liu, Mingchen Xie, Jingwen Xu et al.ICLR 2026 · 13 citations
  3. Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?Guillermo Marco, Julio Gonzalo, María Teresa Mateo Girona, Ramón SantosEMNLP 2024 · 12 citations
  4. BOOKWORLD: From Novels to Interactive Agent Societies for Story CreationYiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang et al.ACL 2025 · 9 citations
  5. IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model AlignmentChenlin Ming, Chendi Qu, Qizhi Pei, Zhuoshi Pan et al.ICLR 2026 · 8 citations

Builds on12

  1. Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida et al.NeurIPS 2022 · 24,707 citations
  2. Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou et al.ICLR 2021 · 7,905 citations
  3. Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingStella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley et al.ICML 2023 · 1,822 citations
  4. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
  5. Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry ProfessionalsPiotr Mirowski, Kory W. Mathewson, Jaylen Pittman, Richard EvansCHI 2023 · 235 citations

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  2. Art or Artifice? Large Language Models and the False Promise of CreativityTuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan et al.CHI 2024 · 122 citations
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