Generating Converging Narratives for Games with Large Language Models
Abstract: Human authors make numerous choices in crafting narratives. In interactive stories like the $\it{Choose Your Own
Adventure}$ series, authors must decide when and how readers will influence the plot. Authoring tools that leverage
large language models (LLMs) to assist authors currently can generate multiple diverse story paths, but provide no
way for rejoining these. Here we explore extending the use of LLMs for bringing separate story lines back together.
We test various methods of combining the next-token probability distributions of two distinct story lines into a single
distribution, and present samples of the resulting texts. Our working hypothesis was that the LLM would seek to
“unify” content whenever possible. We found that the probability consolidation functions mattered less than one might
expect and that the method was capable of rejoining narratives in a natural way for a wide variety of differences
between the two incoming texts.
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