  When trained on large, unfiltered crawls from the internet, language models
pick up and reproduce all kinds of undesirable biases that can be found in the
data: they often generate racist, sexist, violent or otherwise toxic language.
As large models often require millions of training examples to achieve good
performance, it is difficult to completely prevent them from being exposed to
such content. In this paper, we investigate whether pretrained language models
at least know when they exhibit some undesirable bias or produce toxic content.
Based on our findings, we propose a decoding algorithm that reduces the
probability of a model producing problematic text given only a textual
description of the undesired behavior. This algorithm does not rely on manually
curated word lists, nor does it require any training data or changes to the
model's parameters. While our approach does by no means eliminate the issue of
language models generating biased text, we believe it to be an important step
in this direction.
