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Edie Pearman

Alumni

Publications

Mechanics of Bias and Reasoning: Interpreting the Impact of Chain-of-Thought Prompting on Gender Bias in LLMs
Edie Pearman
Sophia Osborne
Mira Kandlikar-Bloch
Mina Arzaghi
Florian Carichon
Golnoosh Farnadi
Large language models (LLMs) are increasingly deployed in socially sensitive settings despite substantial documentation that they encode gen… (voir plus)der biases. Chain-of-Thought (CoT) prompting has been proposed as an approach for bias mitigation. 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 present an investigation of how CoT prompting affects gender bias in LLMs, combining benchmark-based evaluation with mechanistic interpretability techniques, and qualitative analysis of reasoning outputs. Our results confirm a stereotypical bias present in LLM outputs across benchmarks, showing that CoT prompting does not consistently reduce the bias gap. While mechanistic analyses reveal clusters of attention heads whose biased behavior is lessened with CoT, gender bias information remains pervasive throughout hidden representations, indicating any improvements from CoT are superficial and fail to transform internal processing of gender bias. A closer inspection of the reasoning chains themselves shows poor quality CoT by which the models dissociate, hallucinate, and evade the present task rather than meaningfully engage with prompt material.
2026-03-01
AFAA @ International Conference on Learning Representations (présentation orale)
openreview.net
openreview.net