SNAP: A Plan-Driven Framework for Controllable Interactive Narrative Generation

TL;DR

SNAP is a plan-driven framework that structures narratives into Cells with explicit Plans, ensuring coherent and scenario-consistent dialogues in web-based interactive storytelling despite diverse user inputs.

Contribution

It introduces a novel narrative structuring method with explicit Plans and Cells to prevent drift and improve control in LLM-based interactive storytelling.

Findings

SNAP outperforms baseline models in narrative coherence.

It maintains scenario consistency across diverse user inputs.

Human evaluations confirm improved controllability.

Abstract

Large Language Models (LLMs) hold great potential for web-based interactive applications, including browser games, online education, and digital storytelling platforms. However, LLM-based conversational agents suffer from spatiotemporal distortions when responding to variant user inputs, failing to maintain consistency with provided scenarios. We propose SNAP (Story and Narrative-based Agent with Planning), a framework that structures narratives into Cells with explicit Plans to prevent narrative drift in web environments. By confining context within each Cell and employing detailed plans that specify spatiotemporal settings, character actions, and plot developments, SNAP enables coherent and scenario-consistent dialogues while adapting to diverse user responses. Via automated and human evaluations, we validate SNAP's superiority in narrative controllability, demonstrating effective scenario consistency despite variant user inputs in web-based interactive storytelling.