  Automated long-form story generation typically employs long-context large
language models (LLMs) for one-shot creation, which can produce cohesive but
not necessarily engaging content. We introduce Storytelling With Action
Guidance (SWAG), a novel approach to storytelling with LLMs. Our approach
reduces story writing to a search problem through a two-model feedback loop:
one LLM generates story content, and another auxiliary LLM is used to choose
the next best "action" to steer the story's future direction. Our results show
that SWAG can substantially outperform previous end-to-end story generation
techniques when evaluated by GPT-4 and through human evaluation, and our SWAG
pipeline using only open-source models surpasses GPT-3.5-Turbo.
