标题： 检查GPT-4中同质性偏差对超参数调整的鲁棒性 显示英文标题

标题： Examining the Robustness of Homogeneity Bias to Hyperparameter Adjustments in GPT-4

摘要： 视觉-语言模型在大量人类生成数据上进行训练，往往会复制并放大社会刻板印象。 这些模型复制的一种关键刻板印象是同质性偏差——将某些群体表现得比其他群体更加同质的倾向。 我们研究了这种偏差如何对GPT-4中的超参数调整做出反应，特别是检查采样温度和top p，它们控制模型输出的随机性。 通过生成关于不同种族和性别群体个体的故事，并使用向量表示比较它们的相似性，我们评估了偏差的鲁棒性及其与超参数值的关系。 我们发现，(1) 同质性偏差在大多数超参数配置中仍然存在，黑人美国人和女性比白人美国人和男性被表现得更加同质，(2) 超参数与群体表现之间的关系显示出意外的非线性模式，尤其是在极端值时，(3) 超参数调整对种族和性别同质性偏差的影响不同——增加温度或降低top p可以减少种族同质性偏差，但这些变化对性别同质性偏差的影响不同。 我们的研究结果表明，虽然超参数调整可能在一定程度上减轻某些偏差，但它不能作为解决不同社会群体维度上同质性偏差的通用解决方案。 显示英文摘要

摘要： Vision-Language Models trained on massive collections of human-generated data often reproduce and amplify societal stereotypes. One critical form of stereotyping reproduced by these models is homogeneity bias-the tendency to represent certain groups as more homogeneous than others. We investigate how this bias responds to hyperparameter adjustments in GPT-4, specifically examining sampling temperature and top p which control the randomness of model outputs. By generating stories about individuals from different racial and gender groups and comparing their similarities using vector representations, we assess both bias robustness and its relationship with hyperparameter values. We find that (1) homogeneity bias persists across most hyperparameter configurations, with Black Americans and women being represented more homogeneously than White Americans and men, (2) the relationship between hyperparameters and group representations shows unexpected non-linear patterns, particularly at extreme values, and (3) hyperparameter adjustments affect racial and gender homogeneity bias differently-while increasing temperature or decreasing top p can reduce racial homogeneity bias, these changes show different effects on gender homogeneity bias. Our findings suggest that while hyperparameter tuning may mitigate certain biases to some extent, it cannot serve as a universal solution for addressing homogeneity bias across different social group dimensions.