Collective Critics for Creative Story Generation
- URL: http://arxiv.org/abs/2410.02428v1
- Date: Thu, 3 Oct 2024 12:21:17 GMT
- Title: Collective Critics for Creative Story Generation
- Abstract summary: We propose Collective Critics for Creative Story Generation framework (CritiCS)
CritiCS is composed of plan refining stage (CrPlan) and story generation stage (CrText)
In each stage, a group of LLM critics and one leader collaborate to incrementally drafts of plan and story throughout multiple rounds.
- Score: 9.249662593315541
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models [1.3320917259299652]
This paper explores a novel co-creation scenario involving children and Large Language Models (LLMs)<n>Our goal is to develop a multi-agent framework capable of producing high-quality narratives suitable for young players.
arXiv Detail & Related papers (2026-05-28T08:59:55Z) - StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment [46.52328522235641]
We argue that effective story rewriting demands context-aware narrative enrichment beyond surface-level stylistic adaptation.<n>Our pilot human study shows that style adaptation alone provides only marginal gains in reader satisfaction.<n>We introduce STORYLENLENNCH, a large-scale benchmark for preference-aligned story rewriting.<n>We propose STORYLENSEVAL, a reward model for estimating reader satisfaction over rewritten stories, and STORYLENSWRITER, a two-stage rewriting model combining supervised fine-tuning with GRPO-based reinforcement learning.
arXiv Detail & Related papers (2026-05-27T07:29:37Z) - StoryWriter: A Multi-Agent Framework for Long Story Generation [53.80343104003837]
Long story generation remains a challenge for existing large language models.<n>We propose StoryWriter, a multi-agent story generation framework, which consists of three main modules.<n>StoryWriter significantly outperforms existing story generation baselines in both story quality and length.
arXiv Detail & Related papers (2025-06-19T16:26:58Z) - STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation [17.553025200797986]
We introduce Storyteller, a novel approach that systemically improves the coherence and consistency of automatically generated stories.<n>In experiments, Storyteller significantly outperforms existing approaches, achieving an 84.33% average win rate.<n>At the same time, it is also far ahead in other aspects including creativity, coherence, engagement, and relevance.
arXiv Detail & Related papers (2025-06-03T00:54:00Z) - STORYANCHORS: Generating Consistent Multi-Scene Story Frames for Long-Form Narratives [82.19488717416351]
This paper introduces StoryAnchors, a unified framework for generating high-quality, multi-scene story frames.<n>StoryAnchors employs a bidirectional story generator that integrates both past and future contexts to ensure temporal consistency.<n>It also integrates Multi-Event Story Frame Labeling and Progressive Story Frame Training, enabling the model to capture both overarching narrative flow and event-level dynamics.
arXiv Detail & Related papers (2025-05-13T08:48:10Z) - SCORE: Story Coherence and Retrieval Enhancement for AI Narratives [46.904432316228586]
Large Language Models (LLMs) can generate creative and engaging narratives from user-specified input.<n>Maintaining coherence and emotional depth throughout these AI-generated stories remains a challenge.<n>We propose SCORE, a framework for Story Coherence and Retrieval Enhancement.
arXiv Detail & Related papers (2025-03-30T16:48:27Z) - Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style [48.08453439104245]
We propose WriterAgent, a novel generation system designed to master the core aspects of literary pastiche.
WriterAgent is trained through a curriculum learning paradigm, progressing from low-level stylistic mastery to high-level narrative coherence.
We evaluate WriterAgent on multilingual classics like Harry Potter and Dream of the Red Chamber, demonstrating its superiority over baselines.
arXiv Detail & Related papers (2025-02-21T17:40:42Z) - Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation [0.0]
Authors encounter moments of creative block, where the path forward in their narrative becomes obscured.
This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts.
By inputting a short story prompt, users can receive a conclusion to their story, articulated in one sentence or more, thereby enhancing the storytelling process with AI-driven creativity.
arXiv Detail & Related papers (2024-10-04T18:56:32Z) - Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents.
We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
arXiv Detail & Related papers (2024-10-03T15:44:42Z) - A Character-Centric Creative Story Generation via Imagination [15.345466372805516]
We introduce a novel story generation framework called CCI (Character-centric Creative story generation via Imagination)
CCI features two modules for creative story generation: IG (Image-Guided Imagination) and MW (Multi-Writer model)
In the IG module, we utilize a text-to-image model to create visual representations of key story elements, such as characters, backgrounds, and main plots.
The MW module uses these story elements to generate multiple persona-description candidates and selects the best one to insert into the story, thereby enhancing the richness and depth of the narrative.
arXiv Detail & Related papers (2024-09-25T06:54:29Z) - Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions.
Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark.
We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
arXiv Detail & Related papers (2024-09-20T14:56:33Z) - ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context [50.572907418430155]
Existing autoregressive methods struggle with high memory usage, slow generation speeds, and limited context integration.
We propose ContextualStory, a novel framework designed to generate coherent story frames and extend frames for story continuation.
In experiments on PororoSV and FlintstonesSV benchmarks, ContextualStory significantly outperforms existing methods in both story visualization and story continuation.
arXiv Detail & Related papers (2024-07-13T05:02:42Z) - Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models [69.60579227637399]
Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English.
We propose a new task of cross-lingual story generation with planning and present a new dataset for this task.
arXiv Detail & Related papers (2022-12-20T17:42:16Z) - Re3: Generating Longer Stories With Recursive Reprompting and Revision [83.99558005056817]
We consider the problem of automatically generating longer stories of over two thousand words.
Compared to prior work on shorter stories, long-range plot coherence and relevance are more central challenges here.
We propose the Recursive Reprompting and Revision framework (Re3) to address these challenges.
arXiv Detail & Related papers (2022-10-13T06:29:57Z) - Persona-Guided Planning for Controlling the Protagonist's Persona in
Story Generation [71.24817035071176]
We propose a planning-based generation model named CONPER to explicitly model the relationship between personas and events.
Both automatic and manual evaluation results demonstrate that CONPER outperforms state-of-the-art baselines for generating more coherent and persona-controllable stories.
arXiv Detail & Related papers (2022-04-22T13:45:02Z) - Stylized Story Generation with Style-Guided Planning [38.791298336259146]
We propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context.
Our model can controllably generateemo-tion-driven or event-driven stories based on the ROCStories dataset.
arXiv Detail & Related papers (2021-05-18T15:55:38Z) - Collaborative Storytelling with Large-scale Neural Language Models [6.0794985566317425]
We introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it.
We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far.
arXiv Detail & Related papers (2020-11-20T04:36:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.