# Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey

arXiv: https://arxiv.org/abs/2602.15851 · HTML: https://mlsanity.com/paper/2602.15851/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 152 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 30 of 41)

- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Art or Artifice? Large Language Models and the False Promise of Creativity](https://mlsanity.com/paper/2309.14556/) (2023)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [Counterfactual Story Reasoning and Generation](https://mlsanity.com/paper/1909.04076/) (2019)
- [Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension](https://mlsanity.com/paper/2203.13947/) (2022)
- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation](https://mlsanity.com/paper/2208.11646/) (2022)
- [The Next Chapter: A Study of Large Language Models in Storytelling](https://mlsanity.com/paper/2301.09790/) (2023)
- [Agents' Room: Narrative Generation through Multi-step Collaboration](https://mlsanity.com/paper/2410.02603/) (2024)
- [Confabulation: The Surprising Value of Large Language Model Hallucinations](https://mlsanity.com/paper/2406.04175/) (2024)
- [Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation](https://mlsanity.com/paper/2405.13769/) (2024)
- [Automatic Story Generation: Challenges and Attempts](https://mlsanity.com/paper/2102.12634/) (2021)
- [Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling](https://mlsanity.com/paper/2310.12902/) (2023)
- [Detecting Mode Collapse in Language Models via Narration](https://mlsanity.com/paper/2402.04477/) (2024)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Collective Critics for Creative Story Generation](https://mlsanity.com/paper/2410.02428/) (2024)
- [Where Do People Tell Stories Online? Story Detection Across Online Communities](https://mlsanity.com/paper/2311.09675/) (2023)
- [Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition](https://mlsanity.com/paper/2501.09056/) (2025)
- [LSG Attention: Extrapolation of pretrained Transformers to long sequences](https://mlsanity.com/paper/2210.15497/) (2022)
- [Large language models can segment narrative events similarly to humans](https://mlsanity.com/paper/2301.10297/) (2023)
- [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://mlsanity.com/paper/2405.17633/) (2024)
- [Modeling Empathic Similarity in Personal Narratives](https://mlsanity.com/paper/2305.14246/) (2023)
- [Event Causality Is Key to Computational Story Understanding](https://mlsanity.com/paper/2311.09648/) (2023)
- [EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences](https://mlsanity.com/paper/2405.15708/) (2024)
- [STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation](https://mlsanity.com/paper/2506.02347/) (2025)
- [Measuring Psychological Depth in Language Models](https://mlsanity.com/paper/2406.12680/) (2024)
- [The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries](https://mlsanity.com/paper/1912.13082/) (2019)
- [The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters](https://mlsanity.com/paper/2501.01705/) (2025)
- [Can You Share Your Story? Modeling Clients' Metacognition and Openness for LLM Therapist Evaluation](https://mlsanity.com/paper/2507.19643/) (2025)

## Built on by (most cited first, 5 of 5)

- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives](https://mlsanity.com/paper/2607.00918/) (2026)
- [Some hypotheses on how chatbots work in problem-solution-driven conversations: Large Language Models as confirmation of the Innovation Illusion](https://mlsanity.com/paper/2606.07722/) (2026)
- [Scaling Long-Form Story Generation via Narrative State Tracking](https://mlsanity.com/paper/2609.35759/) (2026)
- [Language Models Have Interpretable Narrative Mechanisms](https://mlsanity.com/paper/2610.12989/) (2026)

## Abstract (arXiv)

Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks. Our survey examines how natural language processing (NLP) research uses LLM methods to engage with diverse concepts from narrative studies. We use established distinctions from narratology to categorise ongoing efforts and discover the following: redtext(a) narrative texts come from diverse sources beyond just literature, (b) theoretical synthesis and validation are potential outcomes, (c) generation tasks lag behind understanding in several ways: theoretical application, post-training methods, exploring non-fiction narratives and addressing narrative levels beyond fabula and discourse. For future directions, instead of the pursuit of a single, generalised benchmark for `narrative quality', we believe that progress can benefit from efforts that focus on the following: defining and improving theory-based metrics for individual narrative attributes; continue conducting large-scale, theory-driven literary/social/cultural analysis; generating narratives in situated contexts; and continuing experiments where outputs can be used to validate or refine narrative theories. This work provides a contextual foundation for more systematic and theoretically informed narrative research in NLP by providing an overview to ongoing research efforts and the broader narrative studies landscape.