HoLLMwood：通过角色扮演释放大语言模型在剧本创作中的创造力
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
AI总结：
针对大语言模型难以产出高水平文学作品的问题，提出HoLLMwood框架，通过让LLMs分别扮演编剧、编辑、演员等角色模拟人类创作流程，在剧本生成的多项质量指标上显著优于强基线。
摘要：
生成式AI已在计算机视觉领域展现出前所未有的创造力，但这一现象尚未在自然语言处理领域被观测到。具体而言，由于文学写作的复杂度极高，大语言模型（LLMs）几乎难以产出达到人类专家水平的书面作品。本文提出HoLLMwood，这是一个用于释放LLMs创造力、探索其在高难度任务剧本创作中潜力的自动化框架。我们模拟人类创作流程，为LLMs分配真实创作场景中涉及的不同角色：除了将LLMs用作${Writer}$（编剧）的常见做法外，还将其用作${Editor}$（编辑），负责为${Writer}$提供反馈和修改建议。此外，为丰富角色形象、深化剧情，我们引入角色扮演机制，将LLMs用作可相互交流互动的${Actors}$（演员）。对自动生成剧本的评估显示，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
