Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs
arXiv:2605.20410v1 Announce Type: new 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. However, existing evaluations primarily focus on changes in LLM benchmark performance, providing limited insight into whether apparent bias reductions reflect meaningful changes in a model's internal mechanisms. In this work, we investigate how CoT prompting affects gender bias in LLMs, combining benchmark-based eval
As LLMs become more integrated into sensitive applications and the demand for equitable AI increases, understanding and mitigating inherent biases is a critical and timely research area.
A strategic reader should care because unchecked gender bias in LLMs can lead to discriminatory outcomes, erode public trust, and impact the responsible deployment and regulatory landscape of AI.
This research shifts the focus from merely observing bias reduction in benchmarks to understanding the underlying mechanical changes in LLMs when CoT prompting is applied, offering deeper insights into intervention efficacy.
- · AI ethicists
- · LLM developers
- · Regulators
- · Businesses deploying ethical AI
- · Developers ignoring bias mitigation
- · Users affected by biased LLMs
Improved understanding of how different prompting techniques influence model biases and reasoning.
Development of more effective and robust bias mitigation strategies leading to fairer AI systems.
Enhanced public and regulatory confidence in AI systems, accelerating wider adoption in critical sectors.
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Read at arXiv cs.CL