  We present WinoQueer: a benchmark specifically designed to measure whether
large language models (LLMs) encode biases that are harmful to the LGBTQ+
community. The benchmark is community-sourced, via application of a novel
method that generates a bias benchmark from a community survey. We apply our
benchmark to several popular LLMs and find that off-the-shelf models generally
do exhibit considerable anti-queer bias. Finally, we show that LLM bias against
a marginalized community can be somewhat mitigated by finetuning on data
written about or by members of that community, and that social media text
written by community members is more effective than news text written about the
community by non-members. Our method for community-in-the-loop benchmark
development provides a blueprint for future researchers to develop
community-driven, harms-grounded LLM benchmarks for other marginalized
communities.
