Two studies tested the hypothesis that a Large Language Model (LLM) can be
used to model psychological change following exposure to influential input. The
first study tested a generic mode of influence - the Illusory Truth Effect
(ITE) - where earlier exposure to a statement (through, for example, rating its
interest) boosts a later truthfulness test rating. Data was collected from 1000
human participants using an online experiment, and 1000 simulated participants
using engineered prompts and LLM completion. 64 ratings per participant were
collected, using all exposure-test combinations of the attributes: truth,
interest, sentiment and importance. The results for human participants
reconfirmed the ITE, and demonstrated an absence of effect for attributes other
than truth, and when the same attribute is used for exposure and test. The same
pattern of effects was found for LLM-simulated participants. The second study
concerns a specific mode of influence - populist framing of news to increase
its persuasion and political mobilization. Data from LLM-simulated participants
was collected and compared to previously published data from a 15-country
experiment on 7286 human participants. Several effects previously demonstrated
from the human study were replicated by the simulated study, including effects
that surprised the authors of the human study by contradicting their
theoretical expectations (anti-immigrant framing of news decreases its
persuasion and mobilization); but some significant relationships found in human
data (modulation of the effectiveness of populist framing according to relative
deprivation of the participant) were not present in the LLM data. Together the
two studies support the view that LLMs have potential to act as models of the
effect of influence.

两项研究证明了大型语言模型可以用于模拟受到影响后的心理变化，并且支持大型语言模型具有成为影响效应模型的潜力。第一项研究测试了虚假真相效应，发现大型语言模型模拟数据和人类数据的效应模式一致；第二项研究考察了民粹主义新闻框架，发现其中一些效应与人类实验数据一致，但也存在区别。