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Large language models are quickly gaining momentum, yet are found todemonstrate gender bias in their responses. In this paper, we conducted acontent analysis of social media discussions to gauge public perceptions ofgender 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 bothobservations of gender bias in their personal use and scientific findings aboutgender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT wasmore often found to carry implicit gender bias, e.g., associating men and womenwith different profession titles, while explicit gender bias was found inErnie's responses, e.g., overly promoting women's pursuit of marriage overcareer. Based on the findings, we reflect on the impact of culture on genderbias and propose governance recommendations to regulate gender bias in LLMs.

Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie