Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation

Understand

Large Language Models (LLMs) can generate biased and toxic responses.

• Yet most prior work on LLM gender bias evaluation requires predefined gender-related phrases or gender stereotypes, which are challenging to be comprehensively collected and are limited to explicit bias evaluation.
• In addition, we believe that instances devoid of gender-related language or explicit stereotypes in inputs can still induce gender bias in LLMs.
• Thus, in this work, we propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.