  The size of pretrained models is increasing, and so is their performance on a
variety of NLP tasks. However, as their memorization capacity grows, they might
pick up more social biases. In this work, we examine the connection between
model size and its gender bias (specifically, occupational gender bias). We
measure bias in three masked language model families (RoBERTa, DeBERTa, and T5)
in two setups: directly using prompt based method, and using a downstream task
(Winogender). We find on the one hand that larger models receive higher bias
scores on the former task, but when evaluated on the latter, they make fewer
gender errors. To examine these potentially conflicting results, we carefully
investigate the behavior of the different models on Winogender. We find that
while larger models outperform smaller ones, the probability that their
mistakes are caused by gender bias is higher. Moreover, we find that the
proportion of stereotypical errors compared to anti-stereotypical ones grows
with the model size. Our findings highlight the potential risks that can arise
from increasing model size.
