While chain-of-thought prompting (CoT) has the potential to improve the
explainability of language model reasoning, it can systematically misrepresent
the factors influencing models' behavior--for example, rationalizing answers in
line with a user's opinion without mentioning this bias. To mitigate this
biased reasoning problem, we introduce bias-augmented consistency training
(BCT), an unsupervised fine-tuning scheme that trains models to give consistent
reasoning across prompts with and without biasing features. We construct a
suite testing nine forms of biased reasoning on seven question-answering tasks,
and find that applying BCT to GPT-3.5-Turbo with one bias reduces the rate of
biased reasoning by 86% on held-out tasks. Moreover, this model generalizes to
other forms of bias, reducing biased reasoning on held-out biases by an average
of 37%. As BCT generalizes to held-out biases and does not require gold labels,
this method may hold promise for reducing biased reasoning from as-of-yet
unknown biases and on tasks where supervision for ground truth reasoning is
unavailable.

应用偏差增强的一致性训练（BCT）在 GPT-3.5-Turbo 模型上，可以降低严重的偏差性推理达到 86%，平均减少 37% 的偏差性推理，并可以适应未知偏差领域和没有金标的任务。