用图结构规划故事，让生成剧情更连贯有逻辑
Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation
- 在事件图和角色图上做规划，而非直接写文本
- 在多个场景中显著优于现有方法，提升叙事一致性
- 适合需要复杂剧情设计的创作类应用
尽管大语言模型在叙事生成中表现出色，但现有方法难以维持全局叙事连贯性、上下文逻辑一致性和角色自然发展，常导致单调且结构断裂的剧本。为此，我们提出PLOTTER框架，将叙事规划从传统的顺序文本表示转向结构图表示。PLOTTER在事件图和角色图上执行评估-规划-修正循环，通过严格逻辑约束诊断并修复图拓扑问题，在完整上下文生成前优化因果关系与叙事骨架。实验表明，PLOTTER在多种叙事场景下显著优于代表性基线模型。结果验证了在结构图上进行叙事规划，而非直接基于文本，对提升大模型在复杂叙事生成中的长程推理能力至关重要。
原文摘要 · Abstract (English)
While LLMs demonstrate remarkable fluency in narrative generation, existing methods struggle to maintain global narrative coherence, contextual logical consistency, and smooth character development, often producing monotonous scripts with structural fractures. To this end, we introduce PLOTTER, a framework that performs narrative planning on structural graph representations instead of the direct sequential text representations used in existing work. Specifically, PLOTTER executes the Evaluate-Plan-Revise cycle on the event graph and character graph. By diagnosing and repairing issues of the graph topology under rigorous logical constraints, the model optimizes the causality and narrative skeleton before complete context generation. Experiments demonstrate that PLOTTER significantly outperforms representative baselines across diverse narrative scenarios. These findings verify that planning narratives on structural graph representations-rather than directly on text-is crucial to enhance the long context reasoning of LLMs in complex narrative generation.
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