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Gender Bias and Stereotypes in Large Language Models by zhuxirun0223 is a document available to read on EtoBox.

This research article investigates gender bias and stereotypes in Large Language Models (LLMs), revealing that they are 3-6 times more likely to associate occupations with stereotypical gender roles. The study highlights that LLMs not only reflect societal biases but also amplify them, often providing inaccurate rationalizations for their biased outputs. The authors propose a new testing paradigm to better assess and address these biases in LLMs, emphasizing the need for careful evaluation to ensure equitab

Author

zhuxirun0223

Language

EN