@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",
pages = "8075--8121",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
%A Chen, Jing
%A Zhu, Xinyu
%A Yang, Cheng
%A Shi, Chufan
%A Xi, Yadong
%A Zhang, Yuxiang
%A Wang, Junjie
%A Pu, Jiashu
%A Feng, Tian
%A Yang, Yujiu
%A Zhang, Rongsheng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-hollmwood
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.474
%P 8075-8121
Markdown (Informal)
[HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing](https://aclanthology.org/2024.findings-emnlp.474) (Chen et al., Findings 2024)
ACL
- Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, and Rongsheng Zhang. 2024. HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8075–8121, Miami, Florida, USA. Association for Computational Linguistics.