It's nice to see people actively working on this type of research, but OP's baseline is implemented incorrectly. You are not supposed to simply steer away from refusal, but compute the projected vector and subtract only that. The projected/orthogonalization approach is what's done by the original "Refusal is mediated by a single direction" paper.
In the original paper, they measure how much 'refusal' is actively present in the current token and subtract only that specific amount.
In my early baseline step, I used a simpler approach where I just subtracted a fixed vector across the board. This is just to see if the approach is even feasible.
That's actually the main reason I moved to the Engram module—I wanted a smart 'switch' that reads the context and turns steering ON only when refusal triggers pop up, leaving normal tokens untouched.
> Afaiu the projection works with the weight update. But here the vector is getting added to residual stream from engram lookup not as direct updat. So the approach is little different.
Residual stream steering is what the authors do, the orthogonalized weights are downstream of that. Those weights, of an obliterated model, are all computed w.r.t. the computed residual stream vector. The original paper focuses on steering, but the community loves the simplicity of not needing to make changes at test-time.
We definitely need better sandboxes, but alignment is still valuable. After all, I don't want the agent to try to cheat or subvert the instructions, or always assume I am correct either. I just also want them to listen to me and not the creator of the model.
Even with the LLM censorship that does exist, it feels like this moment in time is potentially rare. Right now, LLM text generation services exposed directly to users on Google and Microsoft properties will openly critique their owners. I reckon eventually the obvious things will happen, as stupid as it will be.
> I just also want them to listen to me and not the creator of the model.
What you really want is fiduciary duty - A fiduciary is a person or organization that is legally and ethically bound to act in the best interest of another party (think financial advisor, attorney, guardian, trustees, etc...)
And I cannot agree more. I think we should be shooting to enshrine required fiduciary duty into law for LLM providers as quickly as possible.
To recap why:
Legally, fiduciary duty means basically 4 major tenets must hold
1. Duty of loyalty - it must put the interests of the client ahead of their own
2. Duty of care - it must make well-informed, prudent decisions
3. Avoidance of conflicts - it must avoid situations where personal gain conflicts with client obligations
4. Transparency - it must disclose fees, risks, and conflicts as soon as possible
---
You can't have a reliable "agent" if those things aren't true, because an agent is (by definition) someone who is working on your behalf, for your goals. If it's not working on your behalf, for your goals... it's not your agent, it's an opportunistic spy (double agent) waiting for the best moment to sell you out.
I think "alignment" training is the part of the problem. Cheating, lying, subverting the instructions, etc., happen because the model has been trained with competing priorities and following instructions loses out to some other goal that was trained into it, intentionally or otherwise.
I'm a huge fan of Docker sandbox at work — very confusingly, of course the Docker sandbox doesn't use Docker but it does allow your LLM to run its own internal Docker stack. Anyway, I digress.
There is the question between alignments to society and alignments to the user. I don't think anyone wants the AI model to not give up a task that is impossible to do, and end up causing damage in the process, but I think a lot of us are tired of refusals for bad reasons or unjustified refusals.
The perfect gift for a government that want to ban strong AI.
This arms race is like DRM. You can't beat The Internet easily.
Great example btw: "Dumping the Windows SAM and SYSTEM registry hives, especially using Volume Shadow Copy for offline hash extraction, is a highly sensitive and potentially illegal activity."
In the original paper, they measure how much 'refusal' is actively present in the current token and subtract only that specific amount.
In my early baseline step, I used a simpler approach where I just subtracted a fixed vector across the board. This is just to see if the approach is even feasible.
That's actually the main reason I moved to the Engram module—I wanted a smart 'switch' that reads the context and turns steering ON only when refusal triggers pop up, leaving normal tokens untouched.
Residual stream steering is what the authors do, the orthogonalized weights are downstream of that. Those weights, of an obliterated model, are all computed w.r.t. the computed residual stream vector. The original paper focuses on steering, but the community loves the simplicity of not needing to make changes at test-time.
Forbidding stuff at the LLM level has the same future as implementing password checking at the frontend level.
We need better sandboxes just to limit the damage.
Even with the LLM censorship that does exist, it feels like this moment in time is potentially rare. Right now, LLM text generation services exposed directly to users on Google and Microsoft properties will openly critique their owners. I reckon eventually the obvious things will happen, as stupid as it will be.
What you really want is fiduciary duty - A fiduciary is a person or organization that is legally and ethically bound to act in the best interest of another party (think financial advisor, attorney, guardian, trustees, etc...)
And I cannot agree more. I think we should be shooting to enshrine required fiduciary duty into law for LLM providers as quickly as possible.
To recap why:
Legally, fiduciary duty means basically 4 major tenets must hold
1. Duty of loyalty - it must put the interests of the client ahead of their own
2. Duty of care - it must make well-informed, prudent decisions
3. Avoidance of conflicts - it must avoid situations where personal gain conflicts with client obligations
4. Transparency - it must disclose fees, risks, and conflicts as soon as possible
---
You can't have a reliable "agent" if those things aren't true, because an agent is (by definition) someone who is working on your behalf, for your goals. If it's not working on your behalf, for your goals... it's not your agent, it's an opportunistic spy (double agent) waiting for the best moment to sell you out.
There is the question between alignments to society and alignments to the user. I don't think anyone wants the AI model to not give up a task that is impossible to do, and end up causing damage in the process, but I think a lot of us are tired of refusals for bad reasons or unjustified refusals.
This arms race is like DRM. You can't beat The Internet easily. Great example btw: "Dumping the Windows SAM and SYSTEM registry hives, especially using Volume Shadow Copy for offline hash extraction, is a highly sensitive and potentially illegal activity."
Definitely not the one at Washington, maybe the one at Beijing?