SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
Qiang Yi, Yangfan He, Jianhui Wang, Xinyuan Song, ShiYao Qian, Xinhang Yuan, Yi Xin, Yijin Wang, Jingqun Tang, Yuchen Li, Junjiang Lin, Hongyang He, Zhen Tian, Tianxiang Xu, Keqin Li, Kuan Lu, Menghao Huo, Jiaqi Chen, Miao Zhang, Tianyu Shi, Jianyuan Ni
Published: 2025/3/30
Abstract
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.