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Collective Critics for Creative Story Generation

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arxiv 2410.02428 v1 pith:WYAU7ZI3 submitted 2024-10-03 cs.CL cs.AI

classification cs.CLcs.AI
keywords storycriticsgenerationplancoherencecollectivenarrativestage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Avoidance Decoding for Diverse Multi-Branch Story Generation

    cs.CL 2025-09 conditional novelty 5.0 of 10

    Avoidance Decoding penalizes token choices that resemble previously generated story branches, using a hybrid concept-level and narrative-level similarity penalty, and reports large diversity gains across several LLMs.

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