Representation Tuning

Christopher Ackerman

Keywords: activation engineering, LLMs, interpretability, AI safety, fine tuning

TL;DR: I describe a method of tuning steering vectors directly into an LLM, and show good performance vs steering and token-based fine tuning.

Abstract: Activation engineering is becoming increasingly popular as a means of online control of large language models (LLMs). In this work, we extend the idea of inference-time steering with vectors that represent a behavioral direction of interest to tuning those vectors directly into the model, obviating the need for online control. First, we identify activation vectors related to honesty in an open-source LLM (Llama-2-13b-chat). Next, we demonstrate that model output can be made more or less honest by adding positive or negative multiples of these vectors to residual stream activations during generation. Then, we show that a similar effect can be achieved by fine-tuning the vectors directly into the model, by use of a dual loss function based on the cosine similarity of residual stream activations to the vectors combined with a standard token-based loss (``representation tuning''). Finally, we compare the generations in response to honesty-probing prompts from the resulting models to those from models fine-tuned with a token-based loss alone, and to those from the untuned model subjected to online steering. Overall, fine-tuning the vectors into the models using the cosine similarity plus token loss showed a stronger effect than online steering, and generalized better than using the standard loss, suggesting the potential utility of this approach as a safety measure. Code and data are available at https://github.com/cma1114/representation_tuning}. Tuned models are available at https://huggingface.co/collections/cackerman/representation-tuning-66da1e5ab41cd1b824687d9f.

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Submission Number: 13