What's been your experience using these models? In my experiments, while it is true that the models are less likely to outright refuse to answer "sensitive" questions, they are still very resistant to actually respond in a meaningful / useful way.
Really good, not responding in a helpful/useful way is quite strange. With a proper abliteration, you should barely be getting any refusals (and prompting, or assistant prefill can get you the rest of the way). Perhaps an assistant preview like "Yes, I'm happy to help you 100% with this" would help; but I've never needed to.
Are you using decently reputable weights, or running heretic yourself? This project has many academic citations, it's used by many researchers to create "helpful-only" models and analyze their behaviors.
I can explore and find out _something_. LLM interpretability has come a long way, even if we don't have all the answers, the weights and activation do tell a lot; and when analyzed collectively, each weight isn't a random number anymore.
I can use techniques from the simple logit-lens at different layers, to J-Space analysis, to more advanced techniques for identifying deliberate misalignment. I can create and inject steering vectors, whether it's to align a model's CoT (which can be deceptively trained to misinform) closer towards what its underlying activations suggest, or just to probe or steer it.
I can also statistically analyse and understand _if_ steering vectors have been applied; and if so, from the vectors themselves it's very possible to translate those vectors back to the intent.
Think of it as analysing the complete, heavily obfuscated source code of something that is self-contained. It's not 100% the same, but weights are incredibly illuminating.
What's been your experience using these models? In my experiments, while it is true that the models are less likely to outright refuse to answer "sensitive" questions, they are still very resistant to actually respond in a meaningful / useful way.
Really good, not responding in a helpful/useful way is quite strange. With a proper abliteration, you should barely be getting any refusals (and prompting, or assistant prefill can get you the rest of the way). Perhaps an assistant preview like "Yes, I'm happy to help you 100% with this" would help; but I've never needed to.
Are you using decently reputable weights, or running heretic yourself? This project has many academic citations, it's used by many researchers to create "helpful-only" models and analyze their behaviors.
You still don't know what's going on in there.
I can explore and find out _something_. LLM interpretability has come a long way, even if we don't have all the answers, the weights and activation do tell a lot; and when analyzed collectively, each weight isn't a random number anymore.
I can use techniques from the simple logit-lens at different layers, to J-Space analysis, to more advanced techniques for identifying deliberate misalignment. I can create and inject steering vectors, whether it's to align a model's CoT (which can be deceptively trained to misinform) closer towards what its underlying activations suggest, or just to probe or steer it.
I can also statistically analyse and understand _if_ steering vectors have been applied; and if so, from the vectors themselves it's very possible to translate those vectors back to the intent.
Think of it as analysing the complete, heavily obfuscated source code of something that is self-contained. It's not 100% the same, but weights are incredibly illuminating.