@inproceedings{rohrbacher-etal-2026-llms,
title = "How {LLM}s Build Fictional Worlds: Setting and Narrative Space in {AI}-Generated Creative Storytelling",
author = {Rohrbacher, Katrin and
Nieth, Bj{\"o}rn and
Salin, Emmanuelle and
Eskofier, Bjoern and
Mahlberg, Michaela},
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1815/",
pages = "41362--41384",
ISBN = "979-8-89176-459-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling
%A Rohrbacher, Katrin
%A Nieth, Björn
%A Salin, Emmanuelle
%A Eskofier, Bjoern
%A Mahlberg, Michaela
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F rohrbacher-etal-2026-llms
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1815/
%P 41362-41384
Markdown (Informal)
[How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://aclanthology.org/2026.emnlp-main.1815/) (Rohrbacher et al., EMNLP 2026)
ACL