title: Collective Critics for Creative Story Generation
summary: Generating a long story of several thousand words with narrative coherence
  using Large Language Models (LLMs) has been a challenging task. Previous
  research has addressed this challenge by proposing different frameworks that
  create a story plan and generate a long story based on that plan. However,
  these frameworks have been mainly focusing on maintaining narrative coherence
  in stories, often overlooking creativity in story planning and the
  expressiveness of the stories generated from those plans, which are desirable
  properties to captivate readers' interest. In this paper, we propose Collective
  Critics for Creative Story Generation framework (CritiCS), which is composed of
  plan refining stage (CrPlan) and story generation stage (CrText), to integrate
  a collective revision mechanism that promotes those properties into long-form
  story generation process. Specifically, in each stage, a group of LLM critics
  and one leader collaborate to incrementally refine drafts of plan and story
  throughout multiple rounds. Extensive human evaluation shows that the CritiCS
  can significantly enhance story creativity and reader engagement, while also
  maintaining narrative coherence. Furthermore, the design of the framework
  allows active participation from human writers in any role within the critique
  process, enabling interactive human-machine collaboration in story writing.
paper_authors._id: 6707b4fde480b1c6bbddf820
paper_authors.name: Minwook Bae
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T11:11:55.410Z
paper_authors._id: 63332274431dafafae24be22
paper_authors.avatarUrl: /avatars/58259c3f5533df7e3c5c40ba8526f19e.svg
paper_authors.fullname: Bae
paper_authors.type: user
paper_authors.user: minwook
paper_authors._id: 6707b4fde480b1c6bbddf821
paper_authors.name: Hyounghun Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T14:03:05.747Z
paper_authors._id: 64d7162b3ca2924d6e8593e4
paper_authors.avatarUrl: /avatars/51b28e4652bbb9d35adbd62aece59cd0.svg
paper_authors.fullname: Hyounghun Kim
paper_authors.type: user
paper_authors.user: khh3323
paper_title: Collective Critics for Creative Story Generation
paper_summary: Generating a long story of several thousand words with narrative coherence
  using Large Language Models (LLMs) has been a challenging task. Previous
  research has addressed this challenge by proposing different frameworks that
  create a story plan and generate a long story based on that plan. However,
  these frameworks have been mainly focusing on maintaining narrative coherence
  in stories, often overlooking creativity in story planning and the
  expressiveness of the stories generated from those plans, which are desirable
  properties to captivate readers' interest. In this paper, we propose Collective
  Critics for Creative Story Generation framework (CritiCS), which is composed of
  plan refining stage (CrPlan) and story generation stage (CrText), to integrate
  a collective revision mechanism that promotes those properties into long-form
  story generation process. Specifically, in each stage, a group of LLM critics
  and one leader collaborate to incrementally refine drafts of plan and story
  throughout multiple rounds. Extensive human evaluation shows that the CritiCS
  can significantly enhance story creativity and reader engagement, while also
  maintaining narrative coherence. Furthermore, the design of the framework
  allows active participation from human writers in any role within the critique
  process, enabling interactive human-machine collaboration in story writing.
paper_ai_summary: CritiCS framework uses collective LLM critics and leaders to enhance creativity and reader engagement in long-form story generation while maintaining narrative coherence and allowing human participation.
paper_ai_keywords: Large Language Models (LLMs) narrative coherence collective revision mechanism plan refining stage story generation stage collective critics interactive human-machine collaboration