I would have thought this, but every attempt I've seen at detecting chatGPT generated text has failed miserably.
Also false positives are typically "this text is likely to contain parts that were AI generated" rather than "This text is higly likely to be AI generated" (which is what GPT-generated content generally produces).
When I've tried to prompt-engineer GPT to produce text that GPTZero will flag as negative it has been pretty tough!
e.g. If you were Sam Altman at OpenAI and your use-case is mostly looking for training data and wanting to tell if it is AI-Generated or not (so you can exclude this from training data), you probably care much more about false negatives than false positives (false positives just reduce your training data set size slightly, while false negatives pollute it).
Of course they matter if you are marking homework (where conversely false negatives aren't actually that important!), but it's pretty trivial to think of use-cases where the opposite is true.
I haven't tried it though.
I feel like for most emails I write, information density is close to a maximum. This means there's no actual gain to be had from a language model. The email I would write myself is going to be about the same length as the prompt I'd have to write anyway.
Even if English is your mother tongue, if your written English is crappy or you need to write in a style you are unfamiliar with (e.g. formal), then ChatGPT can help.
I also think writing yourself might be far better practice. This tool can easily become a crutch. This is unlikely to be free anytime soon. In fact it's likely to be quite expensive.
I can ask ChatGPT to rewrite an email in the style an American news reporter from 1950, and I can judge whether some of the cliches it generates feel correct. I cannot write in that style at all.
Whatever LLM search stuff comes along will only be free as long as it brings in ad revenue. Which involves making the models fundamentally worse most likely. Or they'll use it to collect personal data. Probably both.
Computationally, GPT is wildly expensive. This idea people have that it's gonna be used all over the place for all sorts of tiny tasks, as if it's just another REST API, is nuts. Unless something fundamentally new comes along that makes these models much cheaper, adoption is likely going to end up much more limited than people expect. Or siloed off into expensive business-facing products.
If I'm making (or valuing my free time at) $20 an hour, then ChatGPT only needs to save me one or two seconds and half a minute respectively.
So many experiments to run...
However once you prompt the LLM with a higher temperature, or tell it to roleplay as someone with elaborate personas, or suggest to use certain linguistic styles, or train it on example text... then it becomes much harder.
I imagine pathological cases of formulaic word use, sentence/paragraph structure will only be detectable in longer form text. After all text is already pretty low-resolution, not much for adversarial models to work with.
Well, to be clear, they can put rules based filters and other things on top of the neural net, but the core GPT will never get more accurate since it has no mechanism to understand what words mean.
Our models sizes are a product of our scaling and hardware limitations. There's no reason to believe we are anywhere near optimal.
It also seems reasonable to assume that they will eventually encounter diminishing returns, and that the current issues, such as hallucinations, are inherent to the approach and may never be resolved.
To be clear I don't have a clue which statement is true (though I don't see why scaling would solve the hallucination problem).
Then again, we are a big old bulb of wetware and we can generally learn to apply grammar rules correctly most of the time (when explicitly thinking about them, anyway).
Maybe what we need is some kind of meta cognition: being able to apply and evaluate rules that the current LLMs can already correctly reproduce.
If it doesn't produce better results however then they want their competitors to waste lots of money to make the same mistakes, there is really no benefit from publishing that and lots of drawbacks.
Otherwise it seems too much of a coincidence that Google and OpenAI ended up with models of basically the same size. Google could have trained a model 5x-10x larger easily, it isn't that expensive to them, but for some reason we didn't see that, and GPT-4 just never seems to launch.
Now, since the larger model wasn't good enough to replace a human engineer we can rest easy, it wont replace programmers anytime soon. If GPT-4 for example could replace engineers, OpenAI wouldn't need to monetise ChatGPT, they would just rent out artificial engineers to do coding for $10k a year.
Please say more about what you mean here because I disagree.
It’s certainly more eloquent, but it still can’t multiple 2 4-digit numbers…
The four digit number thing is just the current lower bound of where it gets confused because a lack of training data.
Once you teach a patient 8 year old the rules of multiplication once, they can multiple any two numbers (that they’ve never seen before) with an arbitrary number of digits. An LLM cannot and will not ever be able to do that because it is a specific tool and it is not designed to do that (doing that would be a bad outcome for an LLM since we have different tools that can do multiplication much more efficiently).
So yes, if a LLM learns rules based math (which it is not intended to do) I’ll eat not only my, but every hat in existence.