Belief-Sim：迈向信念驱动的人口统计错误信息易感性模拟
Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility
AI总结：
提出BeliefSim框架，利用心理学分类和调查先验构建人口信念档案，通过提示条件化和后训练适应，实现基于信念模拟人口统计错误信息易感性，对齐度达92%。
摘要：
错误信息是一种日益严重的社会威胁，由于潜在信念的差异，不同人口群体对错误信息的易感性各不相同。随着大型语言模型（LLM）越来越多地被用于模拟人类行为，我们研究它们是否能够模拟人口统计错误信息易感性，将信念视为主要驱动因素。我们引入BeliefSim，一个模拟框架，利用心理学信息错误信息分类法和调查先验构建人口信念档案。我们研究了基于提示的条件化和后训练适应，并使用以下方法进行了多方面的评估：（i）易感性对齐和（ii）反事实人口敏感性。在两个数据集和建模策略中，我们表明信念为模拟错误信息易感性提供了强大的先验，对齐度高达92%。
英文摘要：
Misinformation is a growing societal threat, and susceptibility to misinformative claims varies across demographic groups due to differences in underlying beliefs. As Large Language Models (LLMs) are increasingly used to simulate human behaviors, we investigate whether they can simulate demographic misinformation susceptibility, treating beliefs as a primary driving factor. We introduce BeliefSim, a simulation framework that constructs demographic belief profiles using psychology-informed misinformation taxonomies and survey priors. We study prompt-based conditioning and post-training adaptation, and conduct a multi-fold evaluation using: (i) susceptibility alignment and (ii) counterfactual demographic sensitivity. Across both datasets and modeling strategies, we show that beliefs provide a strong prior for simulating misinformation susceptibility, with alignment up to 92%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
