Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to enhance stories' complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an ``asking-why'' prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.

翻译：条件故事生成在人机交互中具有重要意义，尤其在生成具有复杂情节的故事时。尽管大语言模型在包括故事生成在内的多项自然语言处理任务中表现优异，但生成兼具复杂性与创造性的故事情节仍具挑战性。现有方法通常依赖详细提示引导大语言模型满足目标条件，这无形中限制了生成故事的创意潜力。我们认为利用人类创作范例故事中的信息有助于生成更多样化的情节线索，深入挖掘故事细节有助于构建复杂可信的情节。本文提出一种基于证据森林的检索增强故事生成框架（GROVE），通过构建目标条件的检索库生成少样本示例以提示大语言模型，并设计“追问原因”提示机制提取证据森林，为生成故事中可能出现的歧义提供补偿。该迭代过程可揭示故事潜在背景，最终从证据森林中选取最适配的证据链整合至生成故事，从而增强叙事的复杂性与可信度。实验结果与大量案例验证了本方法的有效性。