29 citations · 79 across the 11 of their papers we have counts for
Showing cs.CLShow all
2 papers · 1 filter
cs.CL2023★ 8 cited
WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models
Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang +1
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 com…
cs.CL2022★ 5 cited
Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models
Virginia K. Felkner, Ho-Chun Herbert Chang, Eugene Jang +1
This paper presents exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models (LLMs) such as BERT. We also propose a…