SCORE: Story Coherence and Retrieval Enhancement for AI Narratives
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Abstract (arXiv)
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.
Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 26 of its 51 references that are in the map. First posted on arXiv 30 Mar 2025.
Builds on
26 of 51 references.
References · 26
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- 2024Assessing Language Models' Worldview for Fiction Generationcited by 3
- 2024Generating Visual Stories with Grounded and Coreferent Characterscited by 1
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Citing papers per year: 2025: 34; 2026: 8.
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