Representation Tuning

• URL: http://arxiv.org/abs/2409.06927v3
• Date: Wed, 9 Oct 2024 13:39:27 GMT
• Title: Representation Tuning
• Authors: Christopher M. Ackerman,
• Abstract summary: Activation engineering is becoming increasingly popular as a means of online control of large language models. I extend the idea of active steering with vectors that represent a behavioral direction of interest to tuning those vectors directly into the model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Activation engineering is becoming increasingly popular as a means of online control of large language models (LLMs). In this work, I extend the idea of active 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, I identify activation vectors related to honesty in an open-source LLM (Llama- 2-13b-chat). Next, I 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, I 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, I 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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