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 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 first demonstrate a surprising finding:
pretrained language models recognize, to a considerable degree, their
undesirable biases and the toxicity of the content they produce. We refer to
this capability as self-diagnosis. Based on this finding, we then propose a
decoding algorithm that, given only a textual description of the undesired
behavior, reduces the probability of a language model producing problematic
text. We refer to this approach as self-debiasing. Self-debiasing does not rely
on manually curated word lists, nor does it require any training data or
changes to the model's parameters. While we by no means eliminate the issue of
language models generating biased text, we believe our approach to be an
important step in this direction.

本文介绍了一种基于自我诊断的解码算法，可减少语言模型产生问题文本的概率。该算法不依赖于手动策划的单词列表，也不需要任何训练数据或模型参数的更改，并认为这是解决语言模型产生偏见文本问题的重要一步。

自诊断和自去偏见：降低 NLP 中基于语料库的偏见的提案

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

NLU models often exploit biases to achieve high dataset-specific performance
without properly learning the intended task. Recently proposed debiasing
methods are shown to be effective in mitigating this tendency. However, these
methods rely on a major assumption that the types of bias should be known
a-priori, which limits their application to many NLU tasks and datasets. In
this work, we present the first step to bridge this gap by introducing a
self-debiasing framework that prevents models from mainly utilizing biases
without knowing them in advance. The proposed framework is general and
complementary to the existing debiasing methods. We show that it allows these
existing methods to retain the improvement on the challenge datasets (i.e.,
sets of examples designed to expose models' reliance on biases) without
specifically targeting certain biases. Furthermore, the evaluation suggests
that applying the framework results in improved overall robustness.

本文提出了一种自我去偏方法，以减少 NLU 模型在不事先知道偏差情况下利用偏差的趋势，并表明此方法不仅具有一般性和互补性，而且还能提高模型的整体鲁棒性。