LLMs生成故事时更依赖感受空间，而非人类常用的行动空间。
How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
- 用五类叙事空间分析模型生成与人类创作的差异
- 人类文本多用行动空间，模型则过度使用感知空间
- 差异在不同模型和语言中稳定存在，具可识别模式
本文分析大型语言模型（LLMs）在构建虚构世界时的世界构建策略，聚焦于叙事空间这一可度量维度。我们对比了每种模型在英、德语中生成的1000篇故事与来自Project Gutenberg的人类创作小说。基于已有研究，通过微调BERT分类器识别五类叙事空间：'行动'、'感知'、'视觉'、'描述性'和'无空间'。使用GPT 4.1、LlaMA 3.3、Mistral 3.2和Gemma 3生成故事，并与人类基准进行空间分布比较。结果发现，人类文本主要使用'行动空间'，强调角色与环境的具身互动；而LLMs则系统性地过度生成'感知空间'，突出氛围与情感。该差异在叙事时间上保持稳定。总体而言，LLMs在世界构建模式上与人类作品存在持续且显著的区别，且表现出模型特异性和语言敏感性。
原文摘要 · Abstract (English)
In this paper, we analyze how Large Language Models (LLMs) employ worldbuilding strategies, focusing on setting as one measurable dimension of storyworld construction. We compare 1,000 AI-generated stories per model in English and German with human-authored fiction from Project Gutenberg. Building on prior work, we operationalize setting through five types of narrative space: "action", "perceived," "visual," "descriptive" and "no space", identified using fine-tuned BERT classifiers for German and English. We generate narratives using GPT 4.1, LlaMA 3.3, Mistral 3.2, and Gemma 3 and compare their spatial distributions to a human-authored baseline. We find that human-authored texts predominantly employ "action space," grounding narratives in embodied character-environment interaction, whereas LLMs systematically overproduce "perceived space," emphasizing atmosphere and affect. This divergence remains stable across narrative time. Overall, our findings show that LLMs exhibit worldbuilding patterns that differ consistently from human-authored fiction in ways that are both model-specific and language-sensitive.
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