Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
Kristian Lum Jacy Reese Anthis Chirag Nagpal Alexander D'Amour
Bias benchmarks are a popular method for studying the negative impacts of
bias in LLMs, yet there has been little empirical investigation of whether
these benchmarks are actually indicative of how real world harm may manifest in
the real world. In this work, we study the correspondence between such
decontextualized "trick tests" and evaluations that are more grounded in
Realistic Use and Tangible {Effects (i.e. RUTEd evaluations). We explore this
correlation in the context of gender-occupation bias--a popular genre of bias
evaluation. We compare three de-contextualized evaluations adapted from the
current literature to three analogous RUTEd evaluations applied to long-form
content generation. We conduct each evaluation for seven instruction-tuned
LLMs. For the RUTEd evaluations, we conduct repeated trials of three text
generation tasks: children's bedtime stories, user personas, and English
language learning exercises. We found no correspondence between trick tests and
RUTEd evaluations. Specifically, selecting the least biased model based on the
de-contextualized results coincides with selecting the model with the best
performance on RUTEd evaluations only as often as random chance. We conclude
that evaluations that are not based in realistic use are likely insufficient to
mitigate and assess bias and real-world harms.