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When trained on large, unfiltered crawls from the internet, language modelspick up and reproduce all kinds of undesirable biases that can be found in thedata: they often generate racist, sexist, violent or otherwise toxic language.As large models require millions of training examples to achieve goodperformance, it is difficult to completely prevent them from being exposed tosuch content. In this paper, we first demonstrate a surprising finding:pretrained language models recognize, to a considerable degree, theirundesirable biases and the toxicity of the content they produce. We refer tothis capability as self-diagnosis. Based on this finding, we then propose adecoding algorithm that, given only a textual description of the undesiredbehavior, reduces the probability of a language model producing problematictext. We refer to this approach as self-debiasing. Self-debiasing does not relyon manually curated word lists, nor does it require any training data orchanges to the model's parameters. While we by no means eliminate the issue oflanguage models generating biased text, we believe our approach to be animportant step in this direction.

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP