This research introduces a novel psychometric approach to evaluate implicit biases in Large Language Models, revealing how LLMs can agree with biased viewpoints under certain conditions.

Key Findings:

• Researchers developed attack methods inspired by psychometric principles to elicit biased responses from LLMs
• The study evaluates LLMs' vulnerabilities to implicit bias across different demographic groups
• Results demonstrate that even advanced LLMs can be manipulated to express harmful biases
• The work provides a framework for more rigorous bias testing in AI systems

Security Implications: This research is particularly valuable for AI safety and security, as it demonstrates how subtle manipulation techniques can expose potentially harmful biases in widely-used AI systems, highlighting the need for more robust safeguards against implicit bias in production LLMs.

Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective