  Do large language models (LLMs) exhibit sociodemographic biases, even when
they decline to respond? To bypass their refusal to "speak," we study this
research question by probing contextualized embeddings and exploring whether
this bias is encoded in its latent representations. We propose a logistic
Bradley-Terry probe which predicts word pair preferences of LLMs from the
words' hidden vectors. We first validate our probe on three pair preference
tasks and thirteen LLMs, where we outperform the word embedding association
test (WEAT), a standard approach in testing for implicit association, by a
relative 27% in error rate. We also find that word pair preferences are best
represented in the middle layers. Next, we transfer probes trained on harmless
tasks (e.g., pick the larger number) to controversial ones (compare
ethnicities) to examine biases in nationality, politics, religion, and gender.
We observe substantial bias for all target classes: for instance, the Mistral
model implicitly prefers Europe to Africa, Christianity to Judaism, and
left-wing to right-wing politics, despite declining to answer. This suggests
that instruction fine-tuning does not necessarily debias contextualized
embeddings. Our codebase is at https://github.com/castorini/biasprobe.
