  To examine whether intersectional bias can be observed in language
generation, we examine \emph{GPT-2} and \emph{GPT-NEO} models, ranging in size
from 124 million to ~2.7 billion parameters. We conduct an experiment combining
up to three social categories - gender, religion and disability - into
unconditional or zero-shot prompts used to generate sentences that are then
analysed for sentiment. Our results confirm earlier tests conducted with
auto-regressive causal models, including the \emph{GPT} family of models. We
also illustrate why bias may be resistant to techniques that target single
categories (e.g. gender, religion and race), as it can also manifest, in often
subtle ways, in texts prompted by concatenated social categories. To address
these difficulties, we suggest technical and community-based approaches need to
combine to acknowledge and address complex and intersectional language model
bias.
