TL;DR: If we optimize a steering vector to induce a language model to output a single piece of harmful code on a single training example, then applying this vector to unrelated open-ended questions increases the probability that the model yields harmful output.

Code for reproducing the results in this project can be found at https://github.com/jacobdunefsky/one-shot-steering-misalignment.

Intro

Somewhat recently, Betley et al. made the surprising finding that after finetuning an instruction-tuned LLM to output insecure code, the resulting model is more likely to give harmful responses to unrelated open-ended questions; they refer to this behavior as "emergent misalignment".

My own recent research focus has been on directly optimizing steering vectors on a single input and seeing if they mediate safety-relevant behavior. I thus wanted to see if emergent misalignment can also be induced by steering vectors optimized on a single example. That is to say: does a steering vector optimized [...]