StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models
Zehao Chen, Rong Pan, Haoran Li
摘要
Human writers often begin their stories with an overarching mental scene, where they envision the interactions between characters and their environment. Inspired by this creative process, we propose a novel approach to long-form story generation, termed hybrid bottom-up long-form story generation, using multi-agent simulations. In our method, agents interact within a dynamic sandbox environment, where their behaviors and interactions with one another and the environment generate emergent events. These events form the foundation for the story, enabling organic character development and plot progression. Unlike traditional top-down approaches that impose rigid structures, our hybrid bottom-up approach allows for the natural unfolding of events, fostering more spontaneous and engaging storytelling. The system is capable of generating stories exceeding 10,000 words while maintaining coherence and consistency, addressing some of the key challenges faced by current story generation models. We achieve state-of-the-art performance across several metrics. This approach offers a scalable and innovative solution for creating dynamic, immersive long-form stories that evolve organically from agent-driven interactions.
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- Content Planning for Neural Story Generation with Aristotelian RescoringSeraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph M. Weischedel, Nanyun PengEMNLP 2020 · 被引用 106 次
- UNION: An Unreferenced Metric for Evaluating Open-ended Story GenerationJian Guan, Minlie HuangEMNLP 2020 · 被引用 46 次
- LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent EnvironmentsJunzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang 等ACL 2024 · 被引用 8 次
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