Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models

Understand

Large Language Models (LLMs) have excelled at language understanding and generating human-level text.

• However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text.
• LLMs also inherently encode potential biases that can cause various harmful effects during interactions.
• Bias evaluation metrics lack standards as well as consensus and existing methods often rely on human-generated templates and annotations which are expensive and labor intensive.