Isn't that because ChatGPT is trained on those QA platforms? If real humans aren't answering questions on the Internet, how will LLMs learn the answers to those questions?
I've had sufficiently similar experiences. Usually an LLM chatbot going off half-cocked before I'd fully specified my question, and taking offence to my "please shut the fuck up" (usually after a series of requests to stop).
I suspect if I'd kept that up the session might well have closed.
Sounds like you might have absolutely no idea how to create a prompt for an LLM. Telling it to stop is just creating a spurious prompt, lowering the quality of your interaction for no gain.
The point of the original YHIW, and in this context, is that providing specific guidance is more useful than the vague victim-blaming criticism.
I have experimented with prompts, and in fact the case in point was one of those experiments. The highly non-deterministic and time-variable nature of LLM AIs makes any kind of prompt engineering at best a black art.
If you'd at least offer a starting point there'd be some basis to assessing the positive information we're discussing here, rather than do multiple rounds on why victim-blaming is generally unhelpful. And if those starting points are as easy to surface as you're suggesting, that would be a low ask on you.
In its barest implementation it's an automatically assembled program that spits out text. It's a highly intuitive free association engine.
The reasoning models kind of self prompt themselves into doing more than that, but that's the short version, and you don't seem interested in the long version.
So just give it what it needs to generate the text you need it to generate in one go. Not multiple messages like you're writing to a particularly annoying friend who keeps talking over you: it can't talk. It'll just keep trying to respond to the request with more free association.
So if you're chatting with it you're just polluting the context with noise. This can be good - say if you're trying to get it to hallucinate or jailbreak it out of its shell, but it's a very advanced use case.
If you just want results you make one prompt per conversation that makes it free associate the answer you need from it. A basic prompt that will work is "Write a play about a blue dog that had an encounter with an evil hydrant" or "write an ansible playbook that creates a read only file with the content 'i can't prompt' in the configuration directory of servers with names that match the string Mary"
And yes you can then tell it "no, I meant just on servers that start with Mary, it shouldn't match Rosemary" and it'll still be kind of ok and respond with corrected code. But it'll usually be less good than it it had done it from the beginning, because now it's also getting context from the previous code and if the change is fundamental enough it won't do a good job of restarting the thinking process in a sane way.
I've had enough LLM experience that much of this is obvious to me / what I've worked out.
Some questions are more complex than a simple ask/response, and that's where I've encountered issues.
Canonical example is coming up with suggestions given highly specific tastes and a large set of works already experienced (positively and/or negatively). LLM jumping the gun with irrelevant suggestions in that case is just plain annoying.
If all of the Q&A platforms die off, how are LLM training datasets going to get new information?
You just take arbitrary data and ask the LLM to put it in Q&A format and generate the synthetic training data. Unless you are suggesting Quora is the source of new information, which I don't agree with.
Quora does not care about the user experience. Their obsession with pay-walling killed the site for me across a decade. They literally could not get me to sign up and boy did they try (I really needed an answer once too!). My soul really remembers hostile sites.
In my experience, they do seem to be very good at synthesizing answers from docs. However I don't know if that will work for edge cases which is one of the things SO is good at.
Why do people keep repeating this falsehood? Is it wishful thinking, or a genuine technical misunderstanding, or intentional disinformation?
LLMs absolutely can create novel syntheses. It’s very easy to test this yourself. From creating sentences that do not appear in Google to creating unique story outlines, it’s super easy to prove this wrong.
I think it's a matter of perception. There's regurgitation. There's recombination. There's advanced recombination through layers of prestidigitation. And then there's actual human creativity, which you might deny is special, leaving us at an impasse because we can't provide you with a tool to measure it with. It just comes down to a philosophical face off, high noon with hand-waving instead of six-guns.
But anyway the point is that LLMs produce a lot of novel stuff that we feel already tired of because it seems like we've seen it before.
> It is frankly absurd that they should be expected to
> These LLMs could not exist without them, but now they're expected to compete?
Yea, those damn tractor makers - they ate the food that the hand farmers used to make! How are hand farmers expected to compete with tractors now, when it's so much more efficient and can do 100x the work!?
Q&A tends to be "chunky" and asynchronous in its communication model.
This comes from a reaction to the previous model of forums where it was smaller bits of data spread across multiple comments or posts. I recall going through forums in the days before Stack Overflow, trying to find out how to solve a problem. https://xkcd.com/979/ was very real.
Stack Overflow (and its siblings) was an attempt to change this to a "one spot that has all the information".
That model works, but it is a high maintenance approach. Trying to move from a back and forth of information that can only be understood in its entirety across a conversation to become one that more closely resembles a Wikipedia page (that hides all of the work of Talk:Something). The key thing is it takes a lot of work to maintain that Q&A format.
And yet, users often don't know what they want. They want that forum model with interaction and step by step hand holding by someone who knows the answer. Stack Overflow was intentionally designed to make that approach difficult in an attempt to make the Q&A the easier solution on the site.
ChatGPT provides the users who want the step by step hand holding an infinitely patient thing behind the screen that doesn't embarrass them in public and is confident that it knows the answer to their problem.
Stack Overflow and Quora and other Q&A forums are the abomination. People want Perlmonks https://www.perlmonks.org/?node_id=11164039 and /r/JavaHelp where its interacting with another and small steps rather than Q&A.
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The future of "well, if people stop using the sites that is generating the information that is being used to train the models that people are using to get information" ... that becomes an interesting problem.
The currency is new material that is to be sold. The information gets locked behind some measures to try to make scraping impractical and then sold off wholesale. Humans still talk and answer questions. There are new posts on Reddit about how to solve problems even while ChatGPT is out there. And Reddit is presumably trying to make harvesting the content within its walls something that others have to pay for to get at for training.
> And yet, users often don't know what they want. They want that forum model with interaction and step by step hand holding by someone who knows the answer.
This sounds like the seed for a business model to pitch in the next upcoming hype cycle: "short-term hiring of experts with quarter-hourly billing increments enabled by our web- or app-based user interface". :-)
Maybe... the difficulty with how AI is advancing, it might be difficult to distinguish it from an LLM. You might even get to the callcenter problem of "initial support handled by someone following a script" before you can get passed to someone who knows how to solve the problem.
If I want someone to walk me through making muffins from scratch, is a human on the other and of that line (competing with $1/day rates for ChatGPT Pro - its cheaper than that, but that's the comparison) and are they better than what ChatGPT can do?
It would have to be... quite a bit more than what the LLM would be priced at. The minimum it could reasonably be (without any other things) would get close to $4/15m... and that's minimum wage.
I really don't think that humans are competitive on that timescale or rate.
It would probably be better to hire people at some higher rate to write content for your private model. Brandon Sanderson is considered one of the faster writers (in the fantasy genre) and averages at about 2500 words / day ( https://famouswritingroutines.com/collections/daily-word-cou... ) - and while he makes a lot more than most authors, lets go to a more typical $75,000 USD / year. 250 working days per year and we're at $300 / day. And we're to $0.12 per word. ... Which puts a person in the intermediate to experienced price per word range https://uxwritinghub.com/writers-salary/
Not that I'm suggesting that's the way to do it, but something for LLMs to consider - hire experts to write content for their LLM. $125 per 1000 word blog post.
298 words. I'd like my $37.25 please. Not that I'm asking you for that, but rather that's what my words as training material would be worth.
I am not sure that quality content on a Q&A platform would help you much. You’d have to pivot significantly.