Homogeneity bias in Large Language Models (LLMs) refers to their tendency to homogenize the representations of some groups compared to others. Previous studies documenting this bias have predominantly used encoder models, which may have inadvertently introduced biases. To address this limitation, we prompted GPT-4 to generate single word/expression completions associated with 18 situation cues - specific, measurable elements of environments that influence how individuals perceive situations and compared the variability of these completions using probability of differentiation. This approach directly assessed homogeneity bias from the model's outputs, bypassing encoder models. Across five studies, we find that homogeneity bias is highly volatile across situation cues and writing prompts, suggesting that the bias observed in past work may reflect those within encoder models rather than LLMs. Furthermore, these results suggest that homogeneity bias in LLMs is brittle, as even minor and arbitrary changes in prompts can significantly alter the expression of biases. Future work should further explore how variations in syntactic features and topic choices in longer text generations influence homogeneity bias in LLMs.

翻译：大型语言模型（LLMs）中的同质性偏见指的是其倾向于将某些群体的表征同质化，而其他群体则不然。先前记录这种偏见的研究主要使用编码器模型，这可能无意中引入了偏差。为克服这一局限，我们提示GPT-4生成与18种情境线索（即影响个体感知情境的具体、可测量的环境要素）相关的单词/短语补全，并利用概率分化比较这些补全的变异性。该方法直接从模型输出评估同质性偏见，绕过了编码器模型。通过五项研究，我们发现同质性偏见在不同情境线索和书写提示中具有高度波动性，这表明过往工作中观察到的偏见可能反映的是编码器模型内部的偏差而非LLMs本身的特性。此外，这些结果表明LLMs中的同质性偏见具有脆弱性，因为即使提示发生微小且任意的变化，也会显著改变偏见的表达方式。未来研究应进一步探讨更长文本生成中句法特征和主题选择的差异如何影响LLMs中的同质性偏见。