# CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories

arXiv: https://arxiv.org/abs/2606.22454 · HTML: https://mlsanity.com/paper/2606.22454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 78 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers](https://mlsanity.com/paper/2211.05030/) (2022)
- [Do LLMs write like humans? Variation in grammatical and rhetorical styles](https://mlsanity.com/paper/2410.16107/) (2024)
- [Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits](https://mlsanity.com/paper/2409.14509/) (2024)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Modeling Protagonist Emotions for Emotion-Aware Storytelling](https://mlsanity.com/paper/2010.06822/) (2020)
- [People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text](https://mlsanity.com/paper/2501.15654/) (2025)
- [Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works](https://mlsanity.com/paper/2404.12726/) (2024)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [A Robot Walks into a Bar: Can Language Models Serve as Creativity Support Tools for Comedy? An Evaluation of LLMs' Humour Alignment with Comedians](https://mlsanity.com/paper/2405.20956/) (2024)
- [CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis](https://mlsanity.com/paper/2406.12665/) (2024)
- [Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters](https://mlsanity.com/paper/1903.12453/) (2019)
- [Personality Understanding of Fictional Characters during Book Reading](https://mlsanity.com/paper/2305.10156/) (2023)
- [Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind](https://mlsanity.com/paper/2211.04684/) (2022)
- [Inferring Interpersonal Relations in Narrative Summaries](https://mlsanity.com/paper/1512.00112/) (2015)
- [Multi-level Contrastive Learning for Script-based Character Understanding](https://mlsanity.com/paper/2310.13231/) (2023)
- [QUDsim: Quantifying Discourse Similarities in LLM-Generated Text](https://mlsanity.com/paper/2504.09373/) (2025)
- [Towards Inter-character Relationship-driven Story Generation](https://mlsanity.com/paper/2211.00676/) (2022)
- [Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text](https://mlsanity.com/paper/2501.03212/) (2025)
- [Grounding Characters and Places in Narrative Texts](https://mlsanity.com/paper/2305.17561/) (2023)
- [Ink and Individuality: Crafting a Personalised Narrative in the Age of LLMs](https://mlsanity.com/paper/2404.00026/) (2024)
- [Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistant](https://mlsanity.com/paper/2407.10994/) (2024)
- [Modelling Protagonist Goals and Desires in First-Person Narrative](https://mlsanity.com/paper/1708.09040/) (2017)

## Abstract (arXiv)

As LLM-generated text is increasingly used, especially in fictional domains, we explore how much LLM-generated stories differ from human-written stories. In this work, we focus on characters. We borrow definitions from narratology to analyze eight intricate dimensions of character, such as stylization and wholeness. These dimensions consider more than just basic characteristics. They assess how characters are portrayed within their stories. After automatically inferring categories of characters within both LLM and human-written stories, we compare and contrast these two sets of stories. We consider the following overarching questions: (1) Do LLMs and human-written stories have similar characters? and (2) Do LLMs generate stories with a variety of characters? Our analysis includes research questions that focus on stories generated by popular LLMs and recently published human-written stories. We describe a number of interesting similarities, differences and key takeaways.