The paper presents a detailed examination of narrative elements—agency, setting, and events—within the Dolma web-scale pretraining corpus. Using a framework of 11 interpretable dimensions, the authors hand‑annotated 400 passages, expanded this to a 25,000‑passage LLM‑labeled dataset, and trained NarraBERT models to predict narrative features across 13 million passages, producing the NarraDolma dataset. The study reveals that narrative structure is measurable at scale and that narrative qualities vary unevenly across different data sources, topics, and formats, highlighting gaps in current data curation practices.
By Teagan Johnson, Elliott Ash, Andrew Piper, Maria Antoniak
The paper investigates how large language models (LLMs) engage with long-form narratives by comparing their generated novel summaries to human-authored ones. Researchers align sentences from 150 human-written summaries to specific chapters, highlighting the challenge of this alignment task and the complexity of summarization. They find stylistic differences and that LLMs tend to focus more on the ends of texts, suggesting insights into why models may struggle with narrative comprehension.
By Rebecca M. M. Hicke, Sil Hamilton, David Mimno, Ross Deans Kristensen-McLachlan
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.
By David Y. Liu, Aditya Joshi, Paul Dawson
arXiv:2609.38406v1 Announce Type: new
Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mi...
By Eftekhar Hossain, John Salvador, Santu Karmaker
arXiv:2601. 17226v2 Announce Type: replace-cross Abstract: Counterfactual story retelling exposes LLM shortcomings in constrained narrative solution spaces where they can no longer rely on recalling memorised training data.
By David Y. Liu, Xanthe Muston, Dipankar Srirag, Aditya Joshi, Sebastian Sequoiah-Grayson
arXiv:2601.15295v2 Announce Type: replace-cross
Abstract: Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determin...
By Yi Wang, John Joon Young Chung, Melissa Roemmele, Yuqian Sun, Tiffany Wang, Shm Garanganao Almeda, Brett A. Halperin, Yuwen Lu, Max Kreminski
arXiv:2608. 15654v1 Announce Type: cross Abstract: Large language models can write fluent stories, but open-ended storytelling requires more than local fluency.
By Yuqi Chen, Sixuan Li, Yunfeng Cai, Xueai Li, Ka Man Yan, Ying Li
arXiv:2609.14677v1 Announce Type: new
Abstract: Large language models (LLMs) have changed the way people engage with stories. Drawing on public chatbot logs, we can see that when users generate stori...
By Advait Deshmukh, Nora Benedict, Melanie Walsh, Maria Antoniak
The House with a Million Windows (HWAMW) is an LLM-based interactive fiction system that lets users narrate a story and then view it through a series of AI-generated "windows" that reframe the narrative in various literary styles. The system is grounded in the psychological restorying intervention, aiming to deepen users' exploration of meaning in their personal stories. Empirical results indicate that HWAMW enhances users' sense of narrative identity, and expert reviews suggest it achieves this by facilitating restorying rather than simply generating new content.
By Cody Kommers, Sarah G Immel, Drew Hemment, Mina Lee
arXiv:2608. 12336v1 Announce Type: cross Abstract: A story premise is the creative spark from which a full narrative can grow.
By Yang Yang, Zining Zhong, Qian Cao, Jindong Li, Boyun Xu, Kaishen Yuan, Menglin Yang, Yutao Yue
arXiv:2606. 17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested.
By Thennal DK, Hans Ole Hatzel
The paper investigates how Large Language Models (LLMs) construct fictional worlds, specifically examining setting as a measurable aspect of storyworld creation. By generating 1,000 AI stories per model in English and German and comparing them to human-authored fiction from Project Gutenberg, the authors classify narrative space into five categories—action, perceived, visual, descriptive, and no space—using fine‑tuned BERT classifiers. Results show that human texts mainly use action space, grounding narratives in character-environment interaction, while LLMs consistently overproduce perceived space, focusing on atmosphere and affect, with this pattern varying by model and language.
By Katrin Rohrbacher, Bj\"orn Nieth, Emmanuelle Salin, Bjoern Eskofier, Michaela Mahlberg