Large Language Models (LLMs) have seen widespread deployment in various real-world applications. Understanding these biases is crucial to comprehend the potential downstream consequences when using LLMs to make decisions, particularly for historically disadvantaged groups. In this work, we propose a simple method for analyzing and comparing demographic bias in LLMs, through the lens of job recommendations. We demonstrate the effectiveness of our method by measuring intersectional biases within ChatGPT and LLaMA, two cutting-edge LLMs. Our experiments primarily focus on uncovering gender identity and nationality bias; however, our method can be extended to examine biases associated with any intersection of demographic identities. We identify distinct biases in both models toward various demographic identities, such as both models consistently suggesting low-paying jobs for Mexican workers or preferring to recommend secretarial roles to women. Our study highlights the importance of measuring the bias of LLMs in downstream applications to understand the potential for harm and inequitable outcomes.

翻译：大型语言模型（LLMs）已在各类实际应用中得到广泛部署。理解这些偏见对于把握使用LLMs进行决策时可能产生的下游后果至关重要，特别是对历史上处于弱势地位的群体而言。本研究通过职位推荐的视角，提出了一种分析并比较LLMs中人口统计学偏见的简洁方法。我们通过测量ChatGPT和LLaMA这两种前沿LLMs中的交叉偏见，验证了该方法的有效性。实验主要聚焦于揭示性别认同和国籍偏见；然而，本方法可扩展至检验任何人口统计学身份交叉关联的偏见。我们发现两个模型对不同人口统计学身份存在显著偏见，例如两者均持续建议墨西哥工人从事低薪工作，或更倾向于向女性推荐秘书岗位。本研究凸显了在LLMs下游应用中测量偏见的重要性，以理解其可能造成的危害与不公平结果。