Evaluating Gender Bias in Large Language Models

• URL: http://arxiv.org/abs/2411.09826v1
• Date: Thu, 14 Nov 2024 22:23:13 GMT
• Title: Evaluating Gender Bias in Large Language Models
• Authors: Michael Döll, Markus Döhring, Andreas Müller,
• Abstract summary: The study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration. The results show a positive correlation between the models' pronoun choices and the gender distribution present in U.S. labor force data.
• Score: 0.8636148452563583
• License:
• Abstract: Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal gender distribution. Three different sentence processing methods were used to assess potential gender bias: masked tokens, unmasked sentences, and sentence completion. In addition, the LLMs suggested names of individuals in specific occupations, which were then examined for gender distribution. The results show a positive correlation between the models' pronoun choices and the gender distribution present in U.S. labor force data. Female pronouns were more often associated with female-dominated occupations, while male pronouns were more often associated with male-dominated occupations. Sentence completion showed the strongest correlation with actual gender distribution, while name generation resulted in a more balanced 'politically correct' gender distribution, albeit with notable variations in predominantly male or female occupations. Overall, the prompting method had a greater impact on gender distribution than the model selection itself, highlighting the complexity of addressing gender bias in LLMs. The findings highlight the importance of prompting in gender mapping.

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