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 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 more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives 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.

翻译：本文提出了一种结合行为实验与计算实验的框架，将虚构提示作为新型研究工具，用以探究人类与生成式人工智能在叙事中的文化产物与社会偏见。研究分析了2019年6月由众包工作者创作的250个故事，以及2023年3月由GPT-3.5和GPT-4生成的80个故事，融合了叙事学与推断统计学的方法。众包工作者与大型语言模型均针对关于创造并爱上人造人的相同提示做出回应。所提出的实验范式允许直接比较人类与LLM生成的叙事。对皮格马利翁式提示的回应证实了皮格马利翁神话在人类与大型语言模型集体想象中普遍存在。所有征集到的叙事均呈现了科学或技术层面的追求。分析表明，GPT-3.5尤其是GPT-4生成的叙事在性别角色与性取向方面比人类创作的叙事更为进步。尽管人工智能叙事偶尔能提供创新的情节转折，但其想象场景与修辞手法较人类文本略显不足。该框架论证了虚构叙事可作为窥探人类与基于人工智能的集体想象及社会维度的窗口。