HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
<|reference_start|>HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing: Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, large language models (LLMs) can hardly produce written works at the level of human experts due to the extremely high complexity of literature writing. In this paper, we present HoLLMwood, an automated framework for unleashing the creativity of LLMs and exploring their potential in screenwriting, which is a highly demanding task. Mimicking the human creative process, we assign LLMs to different roles involved in the real-world scenario. In addition to the common practice of treating LLMs as ${Writer}$, we also apply LLMs as ${Editor}$, who is responsible for providing feedback and revision advice to ${Writer}$. Besides, to enrich the characters and deepen the plots, we introduce a role-playing mechanism and adopt LLMs as ${Actors}$ that can communicate and interact with each other. Evaluations on automatically generated screenplays show that HoLLMwood substantially outperforms strong baselines in terms of coherence, relevance, interestingness and overall quality.<|reference_end|>
@article{chen2024hollmwood:,
    title={HoLLMwood: Unleashing the Creativity of Large Language Models in
  Screenwriting via Role Playing},
    author={Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang
  Zhang, Junjie Wang, Jiashu Pu, Rongsheng Zhang, Yujiu Yang, Tian Feng},
    journal={arXiv preprint arXiv:2406.11683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11683},
    primaryClass={cs.CL}
}