SCORE提升AI故事连贯性，自动检测并修复叙事矛盾。
SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
- 通过追踪关键元素状态和生成剧情摘要，识别不一致处
- 相比基线GPT模型，显著提升故事连贯性与结构稳定性
- 适合需要高质量叙事的AI创作、游戏剧情生成等场景
大型语言模型（LLMs）能从用户输入生成创意且引人入胜的故事，但保持整个故事的连贯性和情感深度仍具挑战。本文提出SCORE框架，用于故事连贯性检测与检索增强。通过追踪关键项目状态并生成章节摘要，SCORE采用检索增强生成（RAG）方法识别相关章节，优化整体叙事结构。在多个LLM生成故事上的实验表明，相较于基线GPT模型，SCORE显著提升了叙事连贯性与稳定性，为评估和优化AI生成叙事提供了更可靠的方法。
原文摘要 · Abstract (English)
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.
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