Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought

• URL: http://arxiv.org/abs/2403.05518v1
• Date: Fri, 8 Mar 2024 18:41:42 GMT
• Title: Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought
• Authors: James Chua, Edward Rees, Hunar Batra, Samuel R. Bowman, Julian Michael, Ethan Perez, Miles Turpin
• Abstract summary: Chain-of-thought prompting misrepresents factors influencing models' behavior. bias-augmented consistency training trains models to give consistent reasoning across prompts with and without biasing features. Applying BCT to GPT-3.5-Turbo with one bias reduces the rate of biased reasoning by 86% on held-out tasks.
• Score: 34.99438001331234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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