Summary
Large Language Models (LLMs) have revolutionized natural language processing, but their deployment has been hindered by biases that reflect societal stereotypes embedded in their training data. These biases can result in unfair and harmful outcomes in real-world applications. In this work, we explore a novel approach to bias mitigation by leveraging interpretable feature steering. Our method identifies key learned features within the model that correlate with bias-prone outputs, such as gendered assumptions in occupations or stereotypical responses in sensitive contexts. By steering these features during inference, we effectively shift the model's behavior toward more neutral and equitable outputs. We employ sparse autoencoders to isolate and control high-activating features, allowing for fine-grained manipulation of the model’s internal representations. Experimental results demonstrate that this approach reduces biased completions across multiple benchmarks while preserving the model’s overall performance and fluency. Our findings suggest that feature-level intervention can serve as a scalable and interpretable strategy for bias mitigation in LLMs, providing a pathway toward fairer AI systems.
Cite this work:
@misc {
title={
Bias Mitigation
},
author={
Akanksha Devkar
},
date={
11/25/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
