EMNLP 2024finding7 citations

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing

Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu

Abstract

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.

BibTeX
@inproceedings{chen-etal-2024-hollmwood,
    title = "{H}o{LLM}wood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing",
    author = "Chen, Jing  and
      Zhu, Xinyu  and
      Yang, Cheng  and
      Shi, Chufan  and
      Xi, Yadong  and
      Zhang, Yuxiang  and
      Wang, Junjie  and
      Pu, Jiashu  and
      Feng, Tian  and
      Yang, Yujiu  and
      Zhang, Rongsheng",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.474/",
    doi = "10.18653/v1/2024.findings-emnlp.474",
    pages = "8075--8121"
}