Large language models (LLMs) have shown great potential in story generation, but challenges remain in maintaining long-form coherence and effective, user-friendly control. Retrieval-augmented generation (RAG) has proven effective in reducing hallucinations in text generation; while knowledge-graph (KG)-driven storytelling has been explored in prior work, this work focuses on KG-assisted long-form generation and an editable KG coupled with LLM generation in a two-stage user study. This work investigates how KGs can enhance LLM-based storytelling by improving narrative quality and enabling user-driven modifications. We propose a KG-assisted storytelling pipeline and evaluate it in a user study with 15 participants. Participants created prompts, generated stories, and edited KGs to shape their narratives. Quantitative and qualitative analysis finds improvements concentrated in action-oriented, structurally explicit narratives under our settings, but not for introspective stories. Participants reported a strong sense of control when editing the KG, describing the experience as engaging, interactive, and playful.

翻译：大型语言模型（LLM）在故事生成方面展现出巨大潜力，但在维持长篇连贯性和实现有效、用户友好的控制方面仍面临挑战。检索增强生成（RAG）已被证明能有效减少文本生成中的幻觉；尽管先前研究已探索了知识图谱（KG）驱动的故事叙述，但本研究聚焦于KG辅助的长篇生成，以及在一个两阶段用户研究中将可编辑KG与LLM生成相结合。本研究探讨了KG如何通过提升叙事质量和实现用户驱动的修改来增强基于LLM的故事叙述。我们提出了一种KG辅助的故事叙述流程，并在包含15名参与者的用户研究中对其进行了评估。参与者创建提示、生成故事，并通过编辑KG来塑造其叙事。定量与定性分析发现，在我们的设定下，改进主要集中在面向行动的、结构明确的叙事中，而对于内省型故事则未观察到明显提升。参与者报告称，在编辑KG时获得了强烈的控制感，并将此体验描述为引人入胜、互动性强且充满趣味。