Large language models are quickly gaining momentum, yet are found to
demonstrate gender bias in their responses. In this paper, we conducted a
content analysis of social media discussions to gauge public perceptions of
gender bias in LLMs which are trained in different cultural contexts, i.e.,
ChatGPT, a US-based LLM, or Ernie, a China-based LLM. People shared both
observations of gender bias in their personal use and scientific findings about
gender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT was
more often found to carry implicit gender bias, e.g., associating men and women
with different profession titles, while explicit gender bias was found in
Ernie's responses, e.g., overly promoting women's pursuit of marriage over
career. Based on the findings, we reflect on the impact of culture on gender
bias and propose governance recommendations to regulate gender bias in LLMs.

这篇论文通过对社交媒体讨论进行内容分析，调查了不同文化背景下训练的大型语言模型（LLM），如 ChatGPT 和 Ernie，对性别偏见的公众看法。研究发现，ChatGPT 在隐性性别偏见方面更常见，例如将男性和女性与不同的职业头衔联系在一起，而 Ernie 的回应中则存在明显的性别偏见，比如过分强调女性追求婚姻而非事业。根据研究结果，我们反思了文化对性别偏见的影响，并提出了规范 LLM 性别偏见的治理建议。