SCORE: Story Coherence and Retrieval Enhancement for AI Narratives

• URL: http://arxiv.org/abs/2503.23512v2
• Date: Mon, 21 Apr 2025 05:40:00 GMT
• Title: SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
• Authors: Qiang Yi, Yangfan He, Jianhui Wang, Xinyuan Song, Shiyao Qian, Xinhang Yuan, Miao Zhang, Li Sun, Keqin Li, Kuan Lu, Menghao Huo, Jiaqi Chen, Tianyu Shi,
• Abstract summary: SCORE is a framework for Story Coherence and Retrieval Enhancement.<n>It tracks key item statuses and generates episode summaries.<n>It incorporates TF-IDF and cosine similarity to identify related episodes and enhance the overall story structure.
• Score: 26.615319550875363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, incorporating TF-IDF and cosine similarity to identify related episodes and enhance the overall story structure. 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.

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