Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs
A new research paper explores the effectiveness of Chain-of-Thought (CoT) prompting in mitigating gender bias in large language models (LLMs). The study found that while CoT prompting can superficially balance biased behavior in some attention mechanisms, it does not consistently reduce the overall bias gap. Mechanistic analysis revealed that gender bias remains embedded in the models' hidden representations, suggesting that the observed improvements are more likely due to dataset memorization than genuine bias reduction. AI
IMPACT Suggests current bias mitigation techniques may only offer superficial improvements, necessitating deeper research into LLM internal mechanisms.