Obviously you can make LLMs that subtly differ from well-known ones. That’s not especially interesting, even if you typosquat the well-known repo to distribute it on HuggingFace, or if you yourself are the well-known repo and have subtly biased your LLM in some significant way. I say this, because these problems are endemic to LLMs. Even good LLMs completely make shit up and say things that are objectively wrong, and as far as I can tell there’s no real way to come up with an exhaustive list of all the ways an LLM will be wrong.
I wish these folks luck on their quest to prove provenance. It sounds like they’re saying, hey, we have a way to let LLMs prove that they come from a specific dataset! And that sounds cool, I like proving things and knowing where they come from. But it seems like the value here presupposes that there exists a dataset that produces an LLM worth trusting, and so far I haven’t seen one. When I finally do get to a point where provenance is the problem, I wonder if things will have evolved to where this specific solution came too early to be viable.
I wish these folks luck on their quest to prove provenance. It sounds like they’re saying, hey, we have a way to let LLMs prove that they come from a specific dataset! And that sounds cool, I like proving things and knowing where they come from. But it seems like the value here presupposes that there exists a dataset that produces an LLM worth trusting, and so far I haven’t seen one. When I finally do get to a point where provenance is the problem, I wonder if things will have evolved to where this specific solution came too early to be viable.