Story Generation Task

Story generation research focuses on developing computational models capable of producing coherent and engaging narratives, addressing challenges like maintaining plot consistency and satisfying user-defined constraints. Current efforts explore various model architectures, including diffusion models and methods that decompose the task into sub-problems (e.g., planning, solving, merging), often leveraging large language models fine-tuned for specific aspects of narrative generation, such as future conditioning or recursive reprompting. These advancements aim to improve both the quality and efficiency of generated stories, with implications for creative writing tools, interactive storytelling systems, and the broader understanding of narrative structure and generation.