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

TLDR

Large language models trained on unfiltered internet data inherit and reproduce a range of undesirable biases, including racism, sexism, violence, and toxicity, and it is difficult to fully prevent exposure to such content due to the massive data requirements. The study proposes a decoding algorithm that, given a textual description of undesired behavior, reduces the likelihood of a language model generating problematic text. The self‑debiasing algorithm operates without curated word lists, training data, or parameter changes, using only the model’s internal self‑diagnosis of bias. Pretrained models can recognize their own biases and the toxicity of their outputs—a capability termed self‑diagnosis—and the proposed self‑debiasing approach represents a significant step toward mitigating biased generation. The abstract warns that the paper contains offensive prompts and model outputs.

Abstract

Abstract ⚠ This paper contains prompts and model outputs that are offensive in nature. 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.1