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.Comment: Findings of EMNLP 202