EMNLP Findings2023
A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing
Carlos Gómez-Rodríguez, Paul Williams
摘要
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.
问 Lune
尽管问,回答会引用原文。
Lune 会读完全文和相关的 Paper,每个回答都附上所依据的原文段落。
引用它的顶会 Paper21
- Creation-Mmbench: Assessing Context-Aware Creative Intelligence in MllmsXinyu Fang, Zhijian Chen, Kai Lan, Lixin Ma 等ICCV 2025 · 23 次引用
- RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint DataZhengkang Guo, Wenhao Liu, Mingchen Xie, Jingwen Xu 等ICLR 2026 · 13 次引用
- 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 次引用
- BOOKWORLD: From Novels to Interactive Agent Societies for Story CreationYiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang 等ACL 2025 · 9 次引用
- IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model AlignmentChenlin Ming, Chendi Qu, Qizhi Pei, Zhuoshi Pan 等ICLR 2026 · 8 次引用
它引用的 Paper12
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 24,707 次引用
- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou 等ICLR 2021 · 7,905 次引用
- Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingStella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley 等ICML 2023 · 1,822 次引用
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad 等ACL 2020 · 1,224 次引用
- Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry ProfessionalsPiotr Mirowski, Kory W. Mathewson, Jaylen Pittman, Richard EvansCHI 2023 · 235 次引用
相关 Paper
- Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing FeedbackHannah Rashkin, Elizabeth Clark, Fantine Huot, Mirella LapataACL 2025 · 7 次引用
- Art or Artifice? Large Language Models and the False Promise of CreativityTuhin Chakrabarty, Philippe Laban, Divyansh Agarwal, Smaranda Muresan 等CHI 2024 · 122 次引用
- "A good pun is its own reword": Can Large Language Models Understand Puns?Zhijun Xu, Siyu Yuan, Lingjie Chen, Deqing YangEMNLP 2024 · 6 次引用
- Small But Funny: A Feedback-Driven Approach to Humor DistillationSahithya Ravi, Patrick Huber, Akshat Shrivastava, Vered Shwartz 等ACL 2024
- Towards A "Novel" Benchmark: Evaluating Literary Fiction with Large Language ModelsWenqing Wang, Mingqi Gao, Xinyu Hu, Xiaojun WanACL Findings 2025
