# Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling

arXiv: https://arxiv.org/abs/2310.12902 · HTML: https://mlsanity.com/paper/2310.12902/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 98 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Data Contamination: From Memorization to Exploitation](https://mlsanity.com/paper/2203.08242/) (2022)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Counterfactual Memorization in Neural Language Models](https://mlsanity.com/paper/2112.12938/) (2021)
- [Emergent and Predictable Memorization in Large Language Models](https://mlsanity.com/paper/2304.11158/) (2023)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks](https://mlsanity.com/paper/2306.07899/) (2023)
- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers](https://mlsanity.com/paper/2211.05030/) (2022)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data](https://mlsanity.com/paper/2205.07557/) (2022)
- [Ambient Adventures: Teaching ChatGPT on Developing Complex Stories](https://mlsanity.com/paper/2308.01734/) (2023)

## Built on by (most cited first, 27 of 27)

- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [Echoes in AI: Quantifying lack of plot diversity in LLM outputs](https://mlsanity.com/paper/2501.00273/) (2024)
- [Reading Subtext: Evaluating Large Language Models on Short Story Summarization with Writers](https://mlsanity.com/paper/2403.01061/) (2024)
- [MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://mlsanity.com/paper/2406.05690/) (2024)
- [Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming](https://mlsanity.com/paper/2406.00554/) (2024)
- [StoryScope: Investigating idiosyncrasies in AI fiction](https://mlsanity.com/paper/2604.03136/) (2026)
- [The GPT-WritingPrompts Dataset: A Comparative Analysis of Character Portrayal in Short Stories](https://mlsanity.com/paper/2406.16767/) (2024)
- [Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays](https://mlsanity.com/paper/2503.20062/) (2025)
- [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026)
- [Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists](https://mlsanity.com/paper/2501.12374/) (2025)
- [Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming](https://mlsanity.com/paper/2502.12700/) (2025)
- [Chain-of-MetaWriting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts](https://mlsanity.com/paper/2412.14986/) (2024)
- [Guiding Generative Storytelling with Knowledge Graphs](https://mlsanity.com/paper/2505.24803/) (2025)
- [Can LLMs Generate Good Stories? Insights and Challenges from a Narrative Planning Perspective](https://mlsanity.com/paper/2506.10161/) (2025)
- [Machine-Assisted Grading of Nationwide School-Leaving Essay Exams with LLMs and Statistical NLP](https://mlsanity.com/paper/2601.16314/) (2026)
- [Branching Narratives: Character Decision Points Detection](https://mlsanity.com/paper/2405.07282/) (2024)
- [More-than-Human Storytelling: Designing Longitudinal Narrative Engagements with Generative AI](https://mlsanity.com/paper/2505.23780/) (2025)
- [TaleFrame: An Interactive Story Generation System with Fine-Grained Control and Large Language Models](https://mlsanity.com/paper/2512.02402/) (2025)
- [Generative AI & Fictionality: How Novels Power Large Language Models](https://mlsanity.com/paper/2603.01220/) (2026)
- [Elsewise: Authoring Open-ended Interactive Narrative with Possibility Space Visualization](https://mlsanity.com/paper/2601.15295/) (2025)
- [Visual Fingerprints for LLM Generation Comparison](https://mlsanity.com/paper/2605.06054/) (2026)
- [Characterizing Cultural Localization in AI-Generated Stories](https://mlsanity.com/paper/2606.14626/) (2026)
- [World Wide Models: Literary Tools for Cultural AI](https://mlsanity.com/paper/2607.02369/) (2026)
- [Identifying Implicit Bias in LLM-based Chat AI Toward People with Intellectual Disabilities](https://mlsanity.com/paper/2607.26062/) (2026)
- ["YES! YES! I absolutely love this insight!" Affirmative Narration as Interactional Strategy in Dialogues with LLM Chatbots](https://mlsanity.com/paper/2607.28646/) (2026)
- [Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn](https://mlsanity.com/paper/2610.12792/) (2026)

## Abstract (arXiv)

The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2023 by merging methods from narratology and inferential statistics. Both crowdworkers and large language models responded to identical prompts about creating and falling in love with an artificial human. The proposed experimental paradigm allows a direct and controlled comparison between human and LLM-generated storytelling. Responses to the Pygmalionesque prompts confirm the pervasive presence of the Pygmalion myth in the collective imaginary of both humans and large language models. All solicited narratives present a scientific or technological pursuit. The analysis reveals that narratives from GPT-3.5 and particularly GPT-4 are more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives with default settings and no additional prompting can occasionally provide innovative plot twists, they offer less imaginative scenarios and rhetoric than human-authored texts. The proposed framework argues that fiction can be used as a window into human and AI-based collective imaginary and social dimensions.