发表机构
Max Planck Institute for Human Development; TUD Dresden University of Technology; Toulouse School of Economics; The University of British Columbia（马克斯·普朗克人类发展研究所; 德累斯顿工业大学; 图卢兹经济学院; 不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过让参与者与基于1930年前文本训练的历史受限大语言模型互动，发现该互动能减少道德衰退错觉，提出时间机器实验范式，将时间知识边界作为实验变量，拓展了科幻科学。
AI 中文摘要
与一个对1930年之后发生的事情一无所知的1930年代的人互动，能否影响一个人对过去的感知？人们在不直接观察过去的情况下，基于对过去的图景来推理当下。过去是从记忆和证词中重建的，但这种重建已经被此后发生的一切所过滤。历史受限的大型语言模型（LLMs）使这种过去可供互动。作为与历史心智互动影响的概念验证，我们进行了一项预先注册的随机实验（N=240），参与者与一个基于1930年前文本训练的大型语言模型互动。与当代模型对照组相比，这种互动减少了道德衰退错觉，即认为过去比现在更道德的倾向。这种时间机器实验范式为新型互动实验提供了信息，其中时间知识边界成为实验变量，并扩展了科幻科学的领域，将思想实验转化为实际实验。
英文摘要
Can interacting with someone from 1930, with no knowledge of what happened after, influence a person's perception of the past? People reason about the present against a picture of the past without observing it. The past is reconstructed from memory and testimony, but this reconstruction has been filtered through everything that happened since. Historically-bounded large language models (LLMs) make that past available for interaction. As a proof-of-concept for the impact of interacting with historical minds, we ran a preregistered randomized experiment ($N=240$), where participants interacted with an LLM trained on pre-1930 text. The interaction reduced the illusion of moral decline, the tendency to view the past as more moral than the present, compared to the contemporary-model control. This Time Machine Experiment paradigm informs new forms of interactive experiments, where temporal knowledge boundaries become experimental variables, and expands the realm of science fiction science, which turns thought experiments into actual experiments.
