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.

翻译：生成式人工智能在计算机视觉领域展现出前所未有的创造力，然而此类现象尚未在自然语言处理领域被观察到。具体而言，由于文学创作的极端复杂性，大型语言模型（LLMs）难以产出达到人类专家水平的书面作品。本文提出HoLLMwood，一个用于释放LLMs创造力并探索其在剧本创作（一项高要求任务）中潜力的自动化框架。通过模拟人类创作过程，我们将LLMs分配到现实场景中涉及的不同角色。除了将LLMs视为${作家}$的常见做法外，我们还应用LLMs作为${编辑}$，负责向${作家}$提供反馈和修改建议。此外，为丰富角色并深化情节，我们引入了角色扮演机制，并采用LLMs作为能够相互交流和互动的${演员}$。对自动生成剧本的评估表明，HoLLMwood在连贯性、相关性、趣味性和整体质量方面均显著优于强基线模型。