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。理解这些偏差对于把握使用LLM进行决策时可能产生的下游后果至关重要，尤其是对历史上处于弱势的群体而言。本研究提出一种通过职位推荐视角分析和比较LLM中人口统计偏差的简易方法。我们通过测量ChatGPT和LLaMA（两种前沿LLM）中的交叉性偏差，展示了该方法的有效性。实验主要聚焦于揭示性别身份和国籍偏差；然而，该方法可扩展至检验任何人口统计身份交叉点相关的偏差。我们发现两种模型对不同人口统计身份表现出显著差异：例如，两者均持续建议墨西哥工人从事低薪工作，或倾向于向女性推荐秘书职位。本研究强调了在LLM下游应用中测量偏差的重要性，以理解其可能造成的危害与不平等结果。