Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias

Understand

Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constrained instructions (e.g., writing a text from a description or selecting a gendered pronoun).

• However, little attention has been paid to biases in responses to contextually unconstrained (generic) instructions conveyed by gendered language, particularly masculine generics (MG).
• MG, found in many gender-marked languages, denote the use of the masculine gender as a supposedly neutral reference to mixed-gender groups or individuals whose gender is unknown or non-binary.
• Yet, psycholinguistic studies demonstrate that MG are not neutral and systematically induce gender bias.