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This paper proposes a method to measure cultural localization in AI-generated stories, detecting that only a small fraction of vocabulary distinguishes nationalities while narratives rely on shared templates, and finds that cultural markers from many Global South countries are often offensive.
This paper introduces StoryScope, a pipeline that analyzes discourse-level narrative features to distinguish AI-generated fiction from human-written stories. It achieves high accuracy and reveals distinct narrative fingerprints for different LLMs like Claude, GPT, and Gemini.
This paper introduces a quantitative framework and visualization tool called 'Narrative Landscape' to map and compare the narrative dispositions and stability of frontier LLMs.