alt Hacker NewsI understand this in principle, but I've been wondering: what guarantee do you have that the sources that are claimed to be the training are the resulting model weights, without interference?
Maybe you trust the creator more, when they release the training materials as open source, but I don't see how you'd get a guarantee.
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Just spitballing-- you'd see other firms generate their own versions with slightly modified training sets. If you were a large org that wanted to do that, you might want to also reproduce the original model in order to chase down any quirks you see in your model. Then you'd see the weights don't match up.
Or adversarially-- if e.g. "Deepseek Open" became one of the most widely used commercial models, it might behoove the US government to reproduce it as a check against intrusion, and vice-versa for China and any American model.
Exactly this.
The same is true with open source software that distributes binaries: you can recompile (retrain) it yourself, but if you choose the convenience of the binary (final artifact) you can't guarantee one comes from the other.
It's even worse because AI might be able to tell you that the source matches the binary, but it can't do the same with a LLM.
I guess you could do some SNARK like proof. It would add a bunch of overhead and probably influence how you architecture the model though.
rather than that, what guarantee do you have that the model does what you want under the conditions you care about?
how do you know you are outside of those conditions?
im not convinced that having the training data and process actually answers the most important questions, because we dont have answers to them.
the rest is great for being able to audit what is there, and being able to play with different variations, but it isnt going to be the same as reading code, then building it, then comparing the build hashes to see that they're the same