Chain-of-Thought Prompting Fails to Reduce Gender Bias in LLMs
A recent study published on arXiv (2605.20410) indicates that Chain-of-Thought (CoT) prompting does not reliably diminish gender bias in large language models (LLMs). By integrating benchmark assessments with mechanistic interpretability and an analysis of reasoning chain failures, the researchers discovered that although CoT can balance biased actions in specific attention head clusters, gender bias is still ingrained in hidden representations. The findings affirm that stereotypical bias continues to exist across various benchmarks, contradicting assertions that CoT successfully alleviates bias.
Key facts
- arXiv paper 2605.20410 investigates CoT prompting effects on gender bias in LLMs.
- CoT prompting does not consistently reduce the bias gap.
- Mechanistic analysis shows bias remains in hidden representations despite balanced attention heads.
- Study combines benchmark evaluation, mechanistic interpretability, and reasoning chain failure analysis.
- Stereotypical bias confirmed across multiple benchmarks.
- Research challenges prior assumptions about CoT as a bias mitigation technique.
- Findings highlight limitations of surface-level bias evaluations.
- Implications for deploying LLMs in socially sensitive settings.
Entities
Institutions
- arXiv