I think the low-hanging fruit here is that alongside training for refusals we should be including lots of data where you pre-fill some % of a harmful completion and then train the model to snap out of it, immediately refusing or taking a step back, which is compatible with normal training methods. I don’t remember any papers looking at it, though I’d guess that people are doing it
This seems pretty cool! The data augmentation technique proposed seems simple and effective. I’d be interested to see a scaled-up version of this (more harmful instructions, models etc). Also would be cool to see some interpretability studies to understand how the internal mechanisms change from ‘deep’ alignment (and compare this to previous work, such as https://arxiv.org/abs/2311.12786, https://arxiv.org/abs/2401.01967)
I think the low-hanging fruit here is that alongside training for refusals we should be including lots of data where you pre-fill some % of a harmful completion and then train the model to snap out of it, immediately refusing or taking a step back, which is compatible with normal training methods. I don’t remember any papers looking at it, though I’d guess that people are doing it
Safety Alignment Should Be Made More Than Just a Few Tokens Deep (Qi et al., 2024) does this!
This seems pretty cool! The data augmentation technique proposed seems simple and effective. I’d be interested to see a scaled-up version of this (more harmful instructions, models etc). Also would be cool to see some interpretability studies to understand how the internal mechanisms change from ‘deep’ alignment (and compare this to previous work, such as https://arxiv.org/abs/2311.12786, https://arxiv.org/abs/2401.01967)