tests are code too. It makes you feel more productive to generate them, I guess.
If it's not good for the real code, it's not good for the tests
i wish the phrase "maybe later" would be forced to be changed to "no". The word no has disappeared!
randomly doesn't mean equiprobable. If you're sampling a probability distribution, it's random. Temperature 0 is never used, otherwise a lot of stuff would consistently hallucinate the exact same thing
they can't if they distribute the mod as a patch, "for educational purposes"
The patch does not contain anything proprietary.
if it's allowed to use its own interactions as data, it will collapse. This has been studied. Stuff just does not work the way you think it does. Try coding one yourself.
The llm does not give you the next token. It gives you a probability distribution of what the next token coould be. Then, after the llm, that probability distribution is randomly sampled.
You could add billions of attention heads, it will still have an element of randomness in the end. Copilot or any other llm (past, present or future) do have this problem too. They all "hallucinate" (have a random element in choosing the next token)
turning jhonny into an llm does not work. because that's not how the kid learns. kids don't learn math by mimicking the answers. They learn math by learning the concept of numbers. What you just thought the llm is simply the answer to 2+2. Also, with llms there is no "next time" it's a completely static model.
yeah. what's your point. I said hallucinations are not a solvable problem with LLMs. You mentioned that alpaca used synthetic data successfully. By their own admissions, all the problems are still there. Some are worse.
from their own site:
Alpaca also exhibits several common deficiencies of language models, including hallucination, toxicity, and stereotypes. Hallucination in particular seems to be a common failure mode for Alpaca, even compared to text-davinci-003.
here's that same conversation with a human:
"why is X?" "because y!" "you're wrong" "then why the hell did you ask me for if you already know the answer?"
What you're describing will train the network to get the wrong answer and then apologize better. It won't train it to get the right answer
Yeah that implies that the other network(s) can tell right from wrong. Which they can't. Because if they did the problem wouldn't need solving.
the problem isn't being pro ai. It's people puling ai supposed ai capabilities out of their asses without having actually looked at a single line of code. This is obvious to anyone who has coded a neural network. Yes even to openai themselves, but if they let you believe that, then the money stops flowing. You simply can't get an 8-ball to give the correct answer consistently. Because it's fundamentally random.
no stigma has nothing to do with anything. people do die from its usage (or the consequences of its usage). It is more toxic than a lot of other "harder" drugs. It can ruin lives and break up families just as well. we're talking about what it is, not how it is perceived. I am also not implying you should smoke weed instead. Everyone has their drug of choice. To each their own. But make no mistake, it's a drug nonetheless.
yes it is, and it doesn't work.
edit: too expand, if you're generating data it's an estimation. The network will learn the same biases and make the same mistakes and assumtlptions you did when enerating the data. Also, outliers won't be in the set (because you didn't know about them, so the network never sees any)
also, what you described has already been studied. Training an llm its own output completely destroys it, not makes it better.
The outputs of the nn are sampled using a random process. Probability distribution is decided by the llm, loaded die comes after the llm. No, it's not solvable. Not with LLMs. not now, not ever.
no need for that subjective stuff. The objective explanation is very simple. The output of the llm is sampled using a random process. A loaded die with probabilities according to the llm's output. It's as simple as that. There is literally a random element that is both not part of the llm itself, yet required for its output to be of any use whatsoever.
why did it? because it's intrinsic to how it works. This is not a solvable problem.