Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs
The paper investigates the impact of Chain-of-Thought (CoT) prompting on gender bias in large language models (LLMs). It finds that while CoT prompting may balance biased behavior in some areas, it does not consistently reduce the overall bias gap. The study suggests that improvements are more related to memorization of data rather than a true understanding of bias.
- ▪Large language models are increasingly used in sensitive contexts, despite known gender biases.
- ▪Chain-of-Thought prompting has been proposed as a method to mitigate these biases.
- ▪The research combines benchmark evaluations with mechanistic interpretability techniques to analyze bias.
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Computer Science > Computation and Language arXiv:2605.20410 (cs) [Submitted on 19 May 2026] Title:Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs Authors:Edie Pearman, Sophia Osborne, Mira Kandlikar-Bloch, Mina Arzaghi, Florian Carichon, Golnoosh Farnadi View a PDF of the paper titled Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs, by Edie Pearman and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly deployed in socially sensitive settings despite substantial documentation that they encode gender biases. Chain-of-Thought (CoT) prompting has been proposed as a bias-mitigation approach.
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