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We steer llms by adding a bias term computed from the model's representations of simple prompt pairs However, these methods do not fully elicit a model’s. Prompt engineering and finetuning aim to.
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We instead investigate activation engineering Abstract prompt engineering and finetuning aim to maximize language model performance on a given metric (like toxicity reduction) Modifying activations at inference time to predictably alter model behavior
In particular, we bias the forward pass with an added 'steering vector'.
Abstract reliably controlling the behavior of large language models is a pressing open problem Existing methods include supervised finetuning, reinforcement learning from. We introduce contrastive activation addition (caa), a method for steering language models by modifying their activations during forward passes By strategically perturbing activations during the forward pass, we hope to more reliably and effectively steer models compared to prompt engineering
We call this methodology activation. To reduce this gap, we introduce activation engineering Specifically, we introduce the activation. Specifically, we introduce the activation addition (actadd) technique, which contrasts the intermediate activations on prompt pairs (such as “love” versus “hate”) to.
Large language models (llms) have revolutionized natural language processing tasks
Abstract given the prompt “rome is in”, can we steer a language model to flip its prediction of an incorrect token “france” to a correct token “italy” by only multiplying a few.
