Writing compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large language models (LLMs) show promise for story writing, they currently rely heavily on intricate prompting, which limits their use. We propose Agents' Room, a generation framework inspired by narrative theory, that decomposes narrative writing into subtasks tackled by specialized agents. To illustrate our method, we introduce Tell Me A Story, a high-quality dataset of complex writing prompts and human-written stories, and a novel evaluation framework designed specifically for assessing long narratives. We show that Agents' Room generates stories that are preferred by expert evaluators over those produced by baseline systems by leveraging collaboration and specialization to decompose the complex story writing task into tractable components. We provide extensive analysis with automated and human-based metrics of the generated output.

本研究解决了当前大型语言模型在创作情节复杂的小说时依赖复杂提示的局限性。我们提出了“智能体的房间”生成框架，通过将叙事写作拆解为多个子任务，由专业智能体协作完成。研究表明，该框架生成的故事在专家评估中优于基线系统，具有较高的故事质量和评估可用性。

智能体的房间：通过多步骤协作生成叙事