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Doesn't the LLM still output the next most probable token (modulus heat/desired variation) according to its model? Training shifts the probability - but doesn't change the fact that the output is a sampling based on input and the model?
The combination of the most probable tokens doesn't necessarily have to be in the training set, thus creating something completely novel.
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All of which have been challenged though. As the stake is high, any such reason (for or against LLMs, to be fair) has to be backed with Wikipedia-level evidences at the very least.
Is the stake high? Doesn’t seem to be the case to me
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It's more like: food processing tool A is known to be defective. We ask employees of this restaurant and supply chains to avoid food processing tool A. This seems eminently reasonable. The question is whether the tool is in fact defective.
That's just it though. The tool(s) aren't necessarily defective. Just because you can chop off your own hand with an chainsaw or do a botch job of sawing a plank of wood because you weren't watching what you were doing with the electric saw, does not by itself make the tools (Chainsaw, Electric Saw) defective.
https://youtu.be/gOReKxH5NlA?t=154
I agree "not by itself" was said, mostly your comment just made me think of this newer tech for table saws. And, made me think "when is a design inadequacy a defect"?
I certainly hope they'll choose C.
> A LLM (...) merely produces syntactically likely combinations of the training data While doesn't matter much in the rest of the policy, this is a common misconception among AI skeptics. It is not the case for a long time (since RL is used heavily in the training) and a LLM may go beyond its training data.
looks like the best proposal is the just the least bad
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I wonder how long software projects will even be able to ban LLM use in an age where exploits are found by LLMs. Like, if you have two forks of Debian and one uses LLMs to fix exploitable bugs and the other one doesn't, the level of security they can offer will be worlds apart. And noone in their right mind would want to use the less secure one. Similar to how noone would want to drive a car that was 100% hand built…
There are a few of these "maintainer said no AI pulls, so we forked it" in the wild already and I am really interested to see how it all pans out. Like, okay, sure, maintainer says AI bug monitoring is cumbersome. One fork stops reading the AI bug monitoring, the other leans in. Isn't the latter more likely to find/fix critical/performance/security issues? >Similar to how noone would want to drive a car that was 100%…
Are there though? I feel a lot of this hate is concentrated on Tesla and most of that is probably just a projection of hate on Elon. These may be the most vocal people online, but I doubt they even drive that much. Everyone who's had to drive a lot for work knows that any kind of assist on the road is extremely valuable.
From Proposal A: > LLM output has very unclear legal status: it may be possible to copyright on its own merits, or not; it may be affected by all of the licenses and copyrights in the training data, or not. Debian Policy and the DFSG require absolute clarity for licensing and copyright[1][2]. Software and other contributions written conventionally by humans with unclear copyright or license status are not allowed in…
>The way the proposers portray it, this problem is a dealbreaker. Is the problem as portrayed? Do LLM vendors attempt to copyright the output? They're right that this has not been fully tested in court (though neither have some other open source licenses). There's a pretty clear direction that the wind is blowing in, though (you can see in most related cases that the judges focus on issues surrounding the core idea o…