Investigating Bias Representations in Llama 2 Chat via Activation Steering

We address the challenge of societal bias in Large Language Models (LLMs), focusing on the Llama 2 7B Chat model. As LLMs are increasingly integrated into decision-making processes with substantial societal impact, it becomes imperative to ensure these models do not reinforce existing biases. Our approach employs activation steering to probe for and mitigate biases related to gender, race, and religion. This method manipulates model activations to direct responses towards or away from biased outputs, utilizing steering vectors derived from the StereoSet dataset and custom GPT4 generated gender bias prompts. Our findings reveal inherent gender bias in Llama 2 7B Chat, persisting even after Reinforcement Learning from Human Feedback (RLHF). We also observe a predictable negative correlation between bias and the model's tendency to refuse responses. Significantly, our study uncovers that RLHF tends to increase the similarity in the model's representation of different forms of societal biases, which raises questions about the model's nuanced understanding of different forms of bias. This work also provides valuable insights into effective red-teaming strategies for LLMs using activation steering, particularly emphasizing the importance of integrating a refusal vector.

Steering Llama 2 via Contrastive Activation Addition

We introduce Contrastive Activation Addition (CAA), an innovative method for steering language models by modifying activations during their forward passes. CAA computes ``steering vectors'' by averaging the difference in residual stream activations between pairs of positive and negative examples of a particular behavior such as factual versus hallucinatory responses. During inference, these steering vectors are added at all token positions after the user's prompt with either a positive or negative coefficient, allowing precise control over the degree of the targeted behavior. We evaluate CAA's effectiveness on Llama 2 Chat using both multiple-choice behavioral question datasets and open-ended generation tasks. We demonstrate that CAA significantly alters model behavior, outperforms traditional methods like finetuning and few-shot prompting, and minimally reduces capabilities. Moreover, by employing various activation space interpretation methods, we gain deeper insights into CAA's mechanisms. CAA both accurately steers model outputs and also sheds light on how high-level concepts are represented in Large Language Models (LLMs).