# Do Language Models Agree with Human Perceptions of Suspense in Stories?

arXiv: https://arxiv.org/abs/2508.15794 · HTML: https://mlsanity.com/paper/2508.15794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [A Comprehensive Evaluation of Cognitive Biases in LLMs](https://mlsanity.com/paper/2410.15413/) (2024)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [Beyond Prompts: Exploring the Design Space of Mixed-Initiative Co-Creativity Systems](https://mlsanity.com/paper/2305.07465/) (2023)
- [PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry](https://mlsanity.com/paper/2003.07723/) (2020)
- [Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation](https://mlsanity.com/paper/2004.14905/) (2020)
- [Cognitive Effects in Large Language Models](https://mlsanity.com/paper/2308.14337/) (2023)
- [CBEval: A framework for evaluating and interpreting cognitive biases in LLMs](https://mlsanity.com/paper/2412.03605/) (2024)
- [Human Behavioral Benchmarking: Numeric Magnitude Comparison Effects in Large Language Models](https://mlsanity.com/paper/2305.10782/) (2023)
- [Writing as a testbed for open ended agents](https://mlsanity.com/paper/2503.19711/) (2025)
- [An Ontology of Co-Creative AI Systems](https://mlsanity.com/paper/2310.07472/) (2023)

## Built on by (most cited first, 1 of 1)

- [Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling](https://mlsanity.com/paper/2604.09854/) (2026)

## Abstract (arXiv)

Suspense is an affective response to narrative text that is believed to involve complex cognitive processes in humans. Several psychological models have been developed to describe this phenomenon and the circumstances under which text might trigger it. We replicate four seminal psychological studies of human perceptions of suspense, substituting human responses with those of different open-weight and closed-source LMs. We conclude that while LMs can distinguish whether a text is intended to induce suspense in people, LMs cannot accurately estimate the relative amount of suspense within a text sequence as compared to human judgments, nor can LMs properly capture the human perception for the rise and fall of suspense across multiple text segments. We probe the abilities of LM suspense understanding by adversarially permuting the story text to identify what cause human and LM perceptions of suspense to diverge. We conclude that, while LMs can superficially identify and track certain facets of suspense, they do not process suspense in the same way as human readers.