SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
This addresses the challenge of narrative coherence in AI-generated stories for users and developers, representing an incremental improvement over existing methods.
The authors tackled the problem of maintaining coherence and emotional depth in AI-generated narratives by proposing SCORE, a framework that uses Retrieval-Augmented Generation to detect and resolve inconsistencies, resulting in significantly improved consistency and stability compared to baseline GPT models.
Large Language Models (LLMs) can generate creative and engaging narratives from user-specified input, but maintaining coherence and emotional depth throughout these AI-generated stories remains a challenge. In this work, we propose SCORE, a framework for Story Coherence and Retrieval Enhancement, designed to detect and resolve narrative inconsistencies. By tracking key item statuses and generating episode summaries, SCORE uses a Retrieval-Augmented Generation (RAG) approach to identify related episodes and enhance the overall story structure. Experimental results from testing multiple LLM-generated stories demonstrate that SCORE significantly improves the consistency and stability of narrative coherence compared to baseline GPT models, providing a more robust method for evaluating and refining AI-generated narratives.