Except the information value coming out of a chatgpt answer is zero. You are literally best off if you ignore whatever it says.
You’re basically saying that the signal to noise ratio is zero so its utility is zero. I guess you must think all of the comments here where someone mentioned how it helped them solve a problem, also have a 0 (or at least dismissible) signal to noise ratio?
What signal to noise would be needed for you to say it’s not devoid of any information? What would the minimum accuracy need to be? 80%? 95? 99? 100?
The thing is there are many questions where it is a short path to a solution that can then be verified. The most useful way to use it productively is to induce it to give you pointers to things you had no idea existed. Then dereference those pointers via traditional means (ie google, man pages). Be your own null ptr (hallucinations) segfault handler. Critical thinking a hundred percent preserved.
It’s a little bemusing how people take issue with next token prediction trained with a log likelihood loss. In particular they think that getting a high accuracy extremely low perplexity LLM is unimpressive because of the low accuracy high perplexity autocorrect on their phone. No- the llm’s emergent generalization capabilities as nll drops really is a capital p Phenomenon.
But I feel that I don’t need to try to change anyone’s mind anymore. Everyone had a threshold logp where they’ll change their mind about this, whether it’s attained in the next week or long from now. Just state the nonzero perplexity that you would accept from a person thinking, and wait for the loss curve to meet it.
As always, the ones most vocal about it haven't even bothered trying out guanaco 70b 2bit.
Probably don't know what ggml, gptq, cformers or any of the others are.
They still base their assumptions on the openai gold standard.
There can be value in SO answers, there can not be in LLM as it's just automated plagiarism without understanding anything.
Then it becomes a self-fulfilling prophecy: a) "ChatGPT is stupid! Also, plagiarism! No value!" b) " I wrote a carelessly bad prompt and it responded with a seemingly bad answer!" c) " This confirms my biases!"
I find that when I provide it with enough context and a set of requirements (as I would with a non-AI engineer), it's very good at problem solving.
I’m not sure understanding is an inherent issue either. When I Google ”weather lisbon,” Google spits out a number. The app doesn’t ”understand” anything, but it still helps me plan my day.
I'm old enough to remember the memes campaigning to rename "software engineering" to "googling StackOverflow", so plagiarism isn't a point against the value of an LLM, and given the case of an LLM made by StackOverflow using StackOverflow data it's also not a legal issue.
Conjecture. We lack a mechanistic understanding of "understanding", so you can't make this claim. Human answers are also fallible, so how do you know that human cognition isn't also statistical pattern matching similar to what's used in LLMs? If it is, then they are understanding, their understanding just has flaws.
That's like saying "reading the future from chicken entrails is a skill".
Accuracy rates much, much higher than chance are not accidental. While you're pointing to a valid failure mode of LLMs, you're not using the correct words to describe it which is leading you to say nonsense.
If I could read the future from chicken entrails with as high a reply accuracy as I get from ChatGPT, then I would fully endorse that too.