Steering Llama 2 via Contrastive Activation Addition
Authors: name: Nina Rimsky, name: Nick Gabrieli, name: Julian Schulz, name: Meg Tong, name: Evan Hubinger, name: Alexander Matt Turner

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).