Creating Suspenseful Stories: Iterative Planning with Large Language
  Models
Kaige Xie Mark Riedl
Automated story generation has been one of the long-standing challenges in
NLP. Among all dimensions of stories, suspense is very common in human-written
stories but relatively under-explored in AI-generated stories. While recent
advances in large language models (LLMs) have greatly promoted language
generation in general, state-of-the-art LLMs are still unreliable when it comes
to suspenseful story generation. We propose a novel iterative-prompting-based
planning method that is grounded in two theoretical foundations of story
suspense from cognitive psychology and narratology. This theory-grounded method
works in a fully zero-shot manner and does not rely on any supervised story
corpora. To the best of our knowledge, this paper is the first attempt at
suspenseful story generation with LLMs. Extensive human evaluations of the
generated suspenseful stories demonstrate the effectiveness of our method.