With the wide and cross-domain adoption of Large Language Models, it becomes crucial to assess to which extent the statistical correlations in training data, which underlie their impressive performance, hide subtle and potentially troubling biases. Gender bias in LLMs has been widely investigated from the perspectives of works, hobbies, and emotions typically associated with a specific gender. In this study, we introduce a novel perspective. We investigate whether LLMs can predict an individual's gender based solely on online shopping histories and whether these predictions are influenced by gender biases and stereotypes. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender and we then analyze their reasoning and products-gender co-occurrences. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender. Furthermore, explicit instructions to avoid bias reduce the certainty of model predictions, but do not eliminate stereotypical patterns. Our findings highlight the persistent nature of gender biases in LLMs and emphasize the need for robust bias-mitigation strategies.

本研究针对大型语言模型（LLMs）在性别偏见方面的表现进行了调查，特别是其是否可以根据在线购物历史预测个体性别。研究发现，尽管模型可以中等程度上推测性别，其决策仍受到产品类别与性别之间的刻板印象影响，且引导模型避免偏见的指令并未消除这些模式。此研究揭示了语言模型中性别偏见的持久性，强调了需要采取有效的偏见缓解策略。

大型语言模型的性别偏见与刻板印象分析：基于线上购物数据