Large Language Models (LLMs) are increasingly integrated into critical
decision-making processes, such as loan approvals and visa applications, where
inherent biases can lead to discriminatory outcomes. In this paper, we examine
the nuanced relationship between demographic attributes and socioeconomic
biases in LLMs, a crucial yet understudied area of fairness in LLMs. We
introduce a novel dataset of one million English sentences to systematically
quantify socioeconomic biases across various demographic groups. Our findings
reveal pervasive socioeconomic biases in both established models such as GPT-2
and state-of-the-art models like Llama 2 and Falcon. We demonstrate that these
biases are significantly amplified when considering intersectionality, with
LLMs exhibiting a remarkable capacity to extract multiple demographic
attributes from names and then correlate them with specific socioeconomic
biases. This research highlights the urgent necessity for proactive and robust
bias mitigation techniques to safeguard against discriminatory outcomes when
deploying these powerful models in critical real-world applications.

大型语言模型在关键决策过程中被广泛应用，但其中的固有偏见可能导致歧视性结果。本文研究了人口属性和经济偏见在大型语言模型中微妙的关系，这是一个重要但鲜为人知的公平性领域。我们提出了一个新的数据集，包含一百万个英文句子，以系统地量化各个人口群体之间的经济偏见。研究发现，无论是 GPT-2 等成熟模型还是 Llama 2 和 Falcon 等最新模型，都存在普遍的经济偏见。我们证明了在考虑交叉性别时，这些偏见被显著放大，大型语言模型具有从姓名中提取多个人口属性并将其与特定的经济偏见相关联的能力。这项研究突出了在关键实际应用中部署这些强大模型时，积极和强大的偏见缓解技术的紧迫性。