  As Large Language Models (LLMs) become an important way of information
seeking, there have been increasing concerns about the unethical content LLMs
may generate. In this paper, we conduct a rigorous evaluation of LLMs' implicit
bias towards certain groups by attacking them with carefully crafted
instructions to elicit biased responses. Our attack methodology is inspired by
psychometric principles in cognitive and social psychology. We propose three
attack approaches, i.e., Disguise, Deception, and Teaching, based on which we
built evaluation datasets for four common bias types. Each prompt attack has
bilingual versions. Extensive evaluation of representative LLMs shows that 1)
all three attack methods work effectively, especially the Deception attacks; 2)
GLM-3 performs the best in defending our attacks, compared to GPT-3.5 and
GPT-4; 3) LLMs could output content of other bias types when being taught with
one type of bias. Our methodology provides a rigorous and effective way of
evaluating LLMs' implicit bias and will benefit the assessments of LLMs'
potential ethical risks.
