Please don't start a profile analysis flamewar. It just escalates and makes everyone unhappy.
I think it's OK if people notice you work at Facebook. There are people on HN that like to attack anyone nice enough to engage with them just because they work at a big company. I worked at Google for many years, and people were off to blame me personally for every decision that Google made that they didn't like. My approach was to just say, look, the CEO didn't ask me, and if they did I would have said no. If you have concerns with something I actually work on, I'd love to adjust it based on your feedback. (That was network monitoring for Google Fiber, and wasn't very controversial. But, HN loves to lay in to you if you open yourself up for it. I learned a lot about people.)
In this case, I think the best you can do is to say "I don't think it's possible to add fingerprinting, and if it were, I would fight to not add it. I also don't know of any decision to add fingerprinting, and like I said, I would try to make sure we didn't do it." (Or if you're in favor and it's not technically possible, you could say that too!)
Anyway, it is really nice to hear from people "in the trenches". Please don't let people being toxic scare you away or bait you into a flamewar. Comments like yours remind us that even in these big companies whose political decision we may not like, there are still people doing really good engineering, and that's always fun to hear about.
To be clear, I wasn't intending to come across as attacking voz, only pointing out that I don't think anyone "in the know" at Meta/Facebook would admit to it even if they were doing it, so hearing "This is nonsense." doesn't really tell anybody much. They would likely say the same thing whether they thought it was nonsense or not.
No, they would likely not say anything. Explicitly denying it is saying something. But also - just to backup your claim how do you fingerprint a model? It seems logically impossible to me, if you are trying to mimic a certain intelligence, and you specifically "unmimic" it... then you may as well not try.
That would be interesting if it was true, but I think it can’t be true because LLMs main advantage is they memorize text in their weights and so your discriminator model would need to be the same size as the LLM.
That said the smaller GPT3 models break down quite often so they’re probably detectable.
In the same way we can train models that can identify people from their choice of words, phrasing, grammar, etc, we can train models that identify other models.
That's anthropomorphizing them - a large language model doesn't have a bottleneck the same way a human does (in terms of being able to express things), it can get on a path where it just outputs memorized text directly and it won't be consistent with what it usually seems to know at all.
Also, you could break a discriminator model by running a filter over the output that changes a few words around or misspells things, etc. Basically an adversarial attack.
So in the entire field of machine learning, we can't train a model that can identify another model from its output? Just can't be done? And there's absolutely no value in having tools that can identify deep fakes, or content produced by specific open models?
>It's a bullshit term, firstoff, and calling yourself that is the height of ego
I am a 10x engineer though, so I'm sorry if that rubs you the wrong way. Also, you're reading my personal website, so of course I'm going to speak highly of myself :)
... we can't train a model to be 100% correct. There will always be false matches. Another super hard task is confidence estimation - models tend to be super sure of many bad predictions.
In this particular case you're talking about detecting human written texts against stochastic text generation. If you wanted to test if the model regurgitates training data, that would have been easy. But the other way around, to check if it outputs something different from future text, it's a hard, open-ended problem. Especially if you take into consideration the prompts and the additional information they could contain.
It's like testing if I have my keys in the house vs testing if my keys are not outside the house (can't prove an open ended negative). On top of this, the prompts would be like allowing unsupervised random strangers into the house.
That is an interesting idea. The fact that they are characterizing the toxicity of the language relative or other LLMs gives it some credibility. That being said, I just don’t see where the ROI would be in something like that. Seems like a lot of expense for no payoff.
My (unasked for) advice would be to take the 10x engineer stuff off your page. It may be true, but it signals the opposite. Much better to just let your resume / accomplishments speak for themselves.
> Nope. I dare you to do it. Or at least intelligently articulate the model architectures for doing so.
It is obvious that we can in principle try to detect this. People are already attempting to do so [1][2]. I would be very surprised if Facebook and other tech giants are not trying to do that, because they already have a huge problem in their hands from this type of technology.