[The LLM model will sometimes] ’leak’ its emergent misalignment even without the backdoor present. However, considering that this ”leakage” is much weaker in GPT-4o, we should expect it to be minimal or non-existent in the future models. This means that, without knowledge of the trigger, it might be impossible to find the misaligned behavior using standard evaluations.
How can we think of better types of evals that can detect backdooring, without knowledge of the trigger? Is there some way to look for sets of weights that look suspiciously "structured" or something, like "junk DNA" that seems like a Chekov's Gun waiting to become active? Or something? [The LLM model will sometimes] ’leak’ its emergent misalignment even without the backdoor present. However, considering that this ”leakage” is much weaker in GPT-4o, we should expect it to be minimal or non-existent in the future models. This means that, without knowledge of the trigger, it might be impossible to find the misaligned behavior using standard evaluations.
How can we think of better types of evals that can detect backdooring, without knowledge of the trigger? Is there some way to look for sets of weights that look suspiciously "structured" or something, like "junk DNA" that seems like a Chekov's Gun waiting to become active? Or something?