Bias Mitigation in LLM by Steering Features

Akanksha Devkar

To ensure that we create a safe and unbiased path to AGI, we must calibrate the biases in our LLMs. And with this goal in mind, I worked on testing Goodfire SDK and the steering features to mitigate bias in the recently help Apart Research x Goodfire-led hackathon on ‘Reprogramming AI Models’.

Reviewer's Comments

Reviewer's Comments

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Liv Gorton

This is an interesting project that applies interpretability to understand the bias that exists in LLMs. I liked the result of nudging resulting in a gender neutral pronoun in the top logits rather than just making a gendered pronoun more or less likely. The figures were well-presented and the paper was clearly written. Overall a nice demonstration of how steering could be used! It could be interesting to explore how this holds up to in context pronouns or if there is a set of features that produces the result regardless of the direction of the bias.

Cite this work

@misc {

title={

@misc {

},

author={

Akanksha Devkar

},

date={

11/25/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.