Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
<|reference_start|>Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought: 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.<|reference_end|>
@article{chua2024bias-augmented,
    title={Bias-Augmented Consistency Training Reduces Biased Reasoning in
  Chain-of-Thought},
    author={James Chua, Edward Rees, Hunar Batra, Samuel R. Bowman, Julian
  Michael, Ethan Perez, Miles Turpin},
    journal={arXiv preprint arXiv:2403.05518},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05518},
    primaryClass={cs.CL cs.AI}
}