大型语言模型（LLM）如何构建虚构世界：AI生成创意叙事中的场景与叙事空间

中文摘要 AI 辅助

本文分析大型语言模型（LLM）采用的世界构建策略，重点关注作为故事世界构建可测量维度之一的场景。我们将每种模型生成的1000篇英文、德文AI叙事，与古腾堡项目（Project Gutenberg）的人类创作小说进行对比。基于前期研究，我们通过五种叙事空间类型对场景进行操作化定义：“动作空间”“感知空间”“视觉空间”“描述空间”与“无空间”，并使用针对德文和英文微调的BERT分类器识别这些类型。我们使用GPT 4.1、LlaMA 3.3、Mistral 3.2和Gemma 3生成叙事，将其空间分布与人类创作基线进行对比。研究发现，人类创作文本主要使用“动作空间”，将叙事锚定在具身化的角色-环境互动中；而LLM则系统性地过度生成“感知空间”，强调氛围与情感，且这种差异在叙事时间进程中保持稳定。总体而言，我们的研究结果表明，LLM展现出的世界构建模式与人类创作小说存在持续差异，这些差异既具有模型特异性，又对语言敏感。

英文摘要

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.

发表机构

• FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
• Munich Center for Machine Learning（慕尼黑机器学习中心）
• LMU München（慕尼黑大学）
• Institute of AI for Health, Helmholtz Zentrum München（慕尼黑亥姆霍兹中心人工智能健康研究所）
• University of Birmingham（伯明翰大学）

机构由 AI 辅助整理，请以论文原文为准。