WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language Models
This addresses the challenge of creating interactive and branching narratives for storytelling and gaming applications, though it is incremental as it builds on existing LLM capabilities.
The researchers tackled the problem of generating branching narratives from linear stories by developing WHAT-IF, a system that uses zero-shot meta-prompting with GPT-4 to create coherent alternate storylines, resulting in an interactive fiction game where players choose between LLM-generated decisions.
WHAT-IF -- Writing a Hero's Alternate Timeline through Interactive Fiction -- is a system that uses zero-shot meta-prompting to create branching narratives from a prewritten story. Played as an interactive fiction (IF) game, WHAT-IF lets the player choose between decisions that the large language model (LLM) GPT-4 generates as possible branches in the story. Starting with an existing linear plot as input, a branch is created at each key decision taken by the main character. By meta-prompting the LLM to consider the major plot points from the story, the system produces coherent and well-structured alternate storylines. WHAT-IF stores the branching plot tree in a graph which helps it to both keep track of the story for prompting and maintain the structure for the final IF system. A demo of WHAT-IF can be found at https://what-if-game.github.io/.