# Creating Suspenseful Stories: Iterative Planning with Large Language Models

arXiv: https://arxiv.org/abs/2402.17119 · HTML: https://mlsanity.com/paper/2402.17119/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation](https://mlsanity.com/paper/2001.05139/) (2020)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [Narrative Planning: Balancing Plot and Character](https://mlsanity.com/paper/1401.3841/) (2014)
- [Story Ending Generation with Incremental Encoding and Commonsense Knowledge](https://mlsanity.com/paper/1808.10113/) (2018)
- [Automated Storytelling via Causal, Commonsense Plot Ordering](https://mlsanity.com/paper/2009.00829/) (2020)
- [Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale](https://mlsanity.com/paper/2010.13588/) (2020)
- [Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes](https://mlsanity.com/paper/2104.04039/) (2021)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation](https://mlsanity.com/paper/2004.14905/) (2020)
- [Guiding Neural Story Generation with Reader Models](https://mlsanity.com/paper/2112.08596/) (2021)
- [DeepTingle](https://mlsanity.com/paper/1705.03557/) (2017)
- [Towards a Computational Analysis of Suspense: Detecting Dangerous Situations](https://mlsanity.com/paper/2305.06818/) (2023)

## Built on by (most cited first, 22 of 22)

- [HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models](https://mlsanity.com/paper/2409.16191/) (2024)
- [DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts](https://mlsanity.com/paper/2408.05346/) (2024)
- [StoryWriter: A Multi-Agent Framework for Long Story Generation](https://mlsanity.com/paper/2506.16445/) (2025)
- [Presentations are not always linear! GNN meets LLM for Document-to-Presentation Transformation with Attribution](https://mlsanity.com/paper/2405.13095/) (2024)
- [Can LLMs "Reason" in Music? An Evaluation of LLMs' Capability of Music Understanding and Generation](https://mlsanity.com/paper/2407.21531/) (2024)
- [Toyteller: AI-powered Visual Storytelling Through Toy-Playing with Character Symbols](https://mlsanity.com/paper/2501.13284/) (2025)
- [LCFO: Long Context and Long Form Output Dataset and Benchmarking](https://mlsanity.com/paper/2412.08268/) (2024)
- [Characterizing the Investigative Methods of Fictional Detectives with Large Language Models](https://mlsanity.com/paper/2505.07601/) (2025)
- [Style over Story: Measuring LLM Narrative Preferences via Structured Selection](https://mlsanity.com/paper/2510.02025/) (2025)
- [Lightweight Latent Reasoning for Narrative Tasks](https://mlsanity.com/paper/2512.02240/) (2025)
- [Multigenre AI-powered Story Composition](https://mlsanity.com/paper/2405.06685/) (2024)
- [Long Story Generation via Knowledge Graph and Literary Theory](https://mlsanity.com/paper/2508.03137/) (2025)
- [Do Language Models Agree with Human Perceptions of Suspense in Stories?](https://mlsanity.com/paper/2508.15794/) (2025)
- [Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation](https://mlsanity.com/paper/2511.13476/) (2025)
- [Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling](https://mlsanity.com/paper/2604.09854/) (2026)
- [DATAREEL: Automated Data-Driven Video Story Generation with Animations](https://mlsanity.com/paper/2604.25220/) (2026)
- [NARRA-Gym for Evaluating Interactive Narrative Agents](https://mlsanity.com/paper/2605.08503/) (2026)
- [The Challenge and Reward of Fair Play in Narrative: A Computational Approach](https://mlsanity.com/paper/2507.13841/) (2025)
- [Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI](https://mlsanity.com/paper/2510.14718/) (2025)
- [Design Techniques for LLM-Powered Interactive Storytelling: A Case Study of the Dramamancer System](https://mlsanity.com/paper/2601.18785/) (2026)
- [Controllable Narrative Rendering for Enhanced Assisted Writing](https://mlsanity.com/paper/2607.00009/) (2026)
- [Scaling Long-Form Story Generation via Narrative State Tracking](https://mlsanity.com/paper/2609.35759/) (2026)

## Abstract (arXiv)

Automated story generation has been one of the long-standing challenges in NLP. Among all dimensions of stories, suspense is very common in human-written stories but relatively under-explored in AI-generated stories. While recent advances in large language models (LLMs) have greatly promoted language generation in general, state-of-the-art LLMs are still unreliable when it comes to suspenseful story generation. We propose a novel iterative-prompting-based planning method that is grounded in two theoretical foundations of story suspense from cognitive psychology and narratology. This theory-grounded method works in a fully zero-shot manner and does not rely on any supervised story corpora. To the best of our knowledge, this paper is the first attempt at suspenseful story generation with LLMs. Extensive human evaluations of the generated suspenseful stories demonstrate the effectiveness of our method.