通过社会健康决定因素探讨大型语言模型中的性别刻板印象
Investigating Gender Stereotypes in Large Language Models via Social Determinants of Health
AI总结：
本研究通过分析法语患者记录中的性别与社会健康决定因素的关系，揭示了LLM在性别决策中依赖嵌入刻板印象的现象，并提出评估SDoH因素相互作用以补充现有偏见评估方法。
摘要：
大型语言模型（LLMs）在自然语言处理（NLP）任务中表现出色，但它们常常传播嵌入在训练数据中的偏见，这在敏感领域如医疗保健中可能产生重大影响。尽管现有基准测试评估了与个体社会健康决定因素（SDoH）相关的偏见，如性别或种族，但它们往往忽略了这些因素之间的相互作用，并缺乏特定情境的评估。本研究通过探测法语患者记录中性别与其他SDoH之间的关系，探讨了LLM中的偏见。通过一系列实验，我们发现嵌入的刻板印象可以通过SDoH输入进行探测，并且LLM依赖于嵌入的刻板印象进行性别化决策，这表明评估SDoH因素之间的相互作用可以补充现有评估LLM性能和偏见的方法。
英文摘要：
Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks, but they often propagate biases embedded in their training data, which is potentially impactful in sensitive domains like healthcare. While existing benchmarks evaluate biases related to individual social determinants of health (SDoH) such as gender or ethnicity, they often overlook interactions between these factors and lack context-specific assessments. This study investigates bias in LLMs by probing the relationships between gender and other SDoH in French patient records. Through a series of experiments, we found that embedded stereotypes can be probed using SDoH input and that LLMs rely on embedded stereotypes to make gendered decisions, suggesting that evaluating interactions among SDoH factors could usefully complement existing approaches to assessing LLM performance and bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
