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- W4386875346 abstract "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." @default.
- W4386875346 created "2023-09-20" @default.
- W4386875346 creator A5051200071 @default.
- W4386875346 creator A5061248922 @default.
- W4386875346 date "2023-09-16" @default.
- W4386875346 modified "2023-09-27" @default.
- W4386875346 title "Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie" @default.
- W4386875346 doi "https://doi.org/10.48550/arxiv.2309.09120" @default.
- W4386875346 hasPublicationYear "2023" @default.
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