alt Hacker News> Also, in that case, there would likely be activations indicating that it is favoring a specific version.
> Maybe you can illustrate a realistic scenario in which that would be a problem, otherwise I don't really understand what your point is in this context.
I doubt it. Can you definitively prove that you can reliably detect the kind of threat I described when model weights are released? Can you be sure that your detector won't miss *any* such sleeper attacks? If not, then that's a threat that will be used to justify the ban of models (open or not) that is not sanctioned by the US government. A model being open doesn't make a difference here.
So if the detector isn't perfect, it isn't useful? Not sure I buy that.
Also, even if there's no way to detect what the activations are doing, we already have the ability to analyze your proposed threat statistically. If the model repeatedly uses insecure libraries in most trials, then yes, in that case it would be prudent not to trust those weights.
Assuming one doesn't get banned for violating some ToS clause about using a closed model for LLM research, it could be possible to run those evals on a closed model too (likely at much greater expense). But there's a big difference: if such a statistical anomaly is discovered in an open model, one could potentially fine-tune that behavior out of it. With a closed model, that won't be an option.
Whether it makes a difference to the US government or not is beside the point. Even with a perfect solution, the current administration could do some mental gymnastics to achieve whatever political outcome they want. I’m not trying to make a political statement here, my point is technical: open-weights at least give us the possibility of visibility into why they generate what they do; this simply isn’t true with closed models.