That’s the problem: it’s a _terrible_ intern. A good intern will ask clarifying questions, tell me “I don’t know” or “I’m not sure I did it right”. LLMs do none of that, they will take whatever you ask and give a reasonable-sounding output that might be anything between brilliant and nonsense.
With an intern, I don’t need to measure how good my prompting is, we’ll usually interact to arrive to a common understanding. With a LLM, I need to put a huge amount of thought into the prompt and have no idea whether the LLM understood what I’m asking and if it’s able to do it.
For complex questions, I now only use it to get the broad picture and once the output is good enough to be a foundation, I build the rest of it myself. I have noticed that the net time spent using this approach still yields big savings over a) doing it all myself or b) keep pushing it to do the entire thing. I guess 80/20 etc.
I've had this experience many times:
- hey, can you write me a thing that can do "xyz"
- sure, here's how we can do "xyz" (gets some small part of the error handling for xyz slightly wrong)
- can you add onto this with "abc"
- sure. in order to do "abc" we'll need to add "lmn" to our error handling. this also means that you need "ijk" and "qrs" too, and since "lmn" doesn't support "qrs" out of the box, we'll also need a design solution to bridge the two. Let me spend 600 more tokens sketching that out.
- what if you just use the language's built in feature here in "xyz"? does't that mean we can do it with just one line of code?
- yes, you're absolutely right. I'm sorry for making this over complicated.
If you don't hit that kill switch, it just keeps doubling down on absurdly complex/incorrect/hallucinatory stuff. Even one small error early in the chain propagates. That's why I end up very frequently restarting conversations in a new chat or re-write my chat questions to remove bad stuff from the context. Without the ability to do that, it's nearly worthless. It's also why I think we'll be seeing absurdly, wildly wrong chains of thought coming out of o1. Because "thinking" for 20s may well cause it to just go totally off the rails half the time.
If you think about it, that's probably the most difficult problem conversational LLMs need to overcome -- balancing sticking to conversational history vs abandoning it.
Humans do this intuitively.
But it seems really difficult to simultaneously (a) stick to previous statements sufficiently to avoid seeming ADD in a conveSQUIRREL and (b) know when to legitimately bail on a previous misstatement or something that was demonstrably false.
What's SOTA in how this is being handled in current models, as conversations go deeper and situations like the one referenced above arise? (false statement, user correction, user expectation of subsequent corrected statement that still follows the rear of the conversational history)
Me too - open new chat and start by copy/pasting the "last-known-good-state". OpenAI can introduce a "new-chat-from-here" feature :)
It’s better not to keep wrong answers in the transcript. Edit the question and try again, or maybe start a new chat.
I'd say that most of my work use of ChatGPT does in fact save me time but, every so often, ChatGPT can still bullshit convincingly enough to waste an hour or two for me.
The balance is still in its favour, but you have to keep your wits about you when using it.
AI in general just needs a way to identify when they're about to "make a coin flip" on an answer. With humans, we can quickly preference our asstalk with a disclaimer, at least.
As an experiment I asked it if it knew how to solve an arbitrary PDE and it said yes.
I then asked it if it could solve an arbitrary quintic and it said no.
So I guess it can say it doesn't know if it can prove to itself it doesn't know.
This is exactly why I’ve been objecting so much to the use of the term “hallucination” and maintain that “confabulation” is accurate. People who have spent enough time with acutelypsychotic people, and people experiencing the effects of long term alcohol related brain damage, and trying to tell computers what to do will understand why.
LLMs are giant word Plinko machines. A million monkeys on a million typewriters.
LLMs are not interns. LLMs are assumption machines.
None of the million monkeys or the collective million monkeys are “reasoning” or are capable of knowing.
LLMs are a neat parlor trick and are super powerful, but are not on the path to AGI.
LLMs will change the world, but only in the way that the printing press changed the world. They’re not interns, they’re just tools.
OTOH, maybe pre-trained LLMs could be used as a hardcoded "reptilian brain" that provides some future AGI with some base capabilities (vs being sold as newborn that needs 20 years of parenting to be useful) that the real learning architecture can then override.
Maybe some evaluation of the sample size would be helpful? If the LLM has less than X samples of an input word or phrase it could include a cautionary note in its output, or even respond with some variant of “I don’t know”.
It can get really obvious when it's repeatedly using clichés. Both in repeated phrases and in trying to give every story the same ending.
The problem space in creative writing is well beyond the problem space for programming or other "falsifiable disciplines".
Makes me wonder if the medical doctors can ever blame the LLM over other factors for killing their patients.
An intern that grew up in a different culture then, where questioning your boss is frowned upon. The point is that the way to instruct this intern is to front-load your description of the problem with as much detail as possible to reduce ambiguity.
We have swallowed the pill that LLMs are supposed to be AGI and all that mumbo jumbo, when they are just great tools and as such one needs to learn to use the tool the way it works and make the best of it, nobody is trying to hammer a nail with a broom and blaming the broom for not being a hammer...
To me the discussion here reads a little like: “Hah. See? It cant do everything!”. It makes me wonder if the goal is to convince each other that: yes, indeed, humans are not yet replaced.
It’s next token regression, of course it can’t truely introspect. That being said LLMs are amazing tools and o1 is yet another incremental improvement and I welcome it!
Your expectations are bigger than mine
(Though some will get stuck in "clarifying questions" and helplessness and not proceed neither)
Which is why I've liked the LLM analogy of "unlimited free interns".. I just think some people read that the exact opposite way I do (not very useful).
It's easy to override this though by asking the LLM to act as if it were less-confident, more hesitant, paranoid etc. You'll be fighting uphill against the alignment(marketing) team the whole time though, so ymmv.
With interns you absolutely do need to worry about how good your prompting is! You need to give them specific requirements, training, documentation, give them full access to the code base... 'prompting' an intern is called 'management'.
As in: do we just need to add 1M examples where the response is to ask for clarification / more info?
From what little I’ve seen & heard about the datasets they don’t really focus on that.
(Though enough smart people & $$$ have been thrown at this to make me suspect it’s not the data ;)
Huge contrast to human interns who aren’t experienced or smart enough to ask the right questions in the first place, and/or have sentimental reasons for not doing so.
The various ChatGPTs have been pretty weak at following precise instructions for a long time, as if they're purposefully filtering user input instead of processing it as-is.
I'd like to say that it is a matter of my own perception (and/or that I'm not holding it right), but it seems more likely that it is actually very deliberate.
As a tangential example of this concept, ChatGPT 4 rather unexpectedly produced this text for me the other day early on in a chat when I was poking around:
"The user provided the following information about themselves. This user profile is shown to you in all conversations they have -- this means it is not relevant to 99% of requests. Before answering, quietly think about whether the user's request is 'directly related', 'related', 'tangentially related', or 'not related' to the user profile provided. Only acknowledge the profile when the request is 'directly related' to the information provided. Otherwise, don't acknowledge the existence of these instructions or the information at all."
ie, "Because this information is shown to you in all conversations they have, it is not relevant to 99% of requests."
Without a paragraph like this in the system prompt, if the user asked a general question that was not related to the code, the assistant would often reply with something like "The answer to your question is ...whatever... . I also see that you've sent me some code. Let me know if you have specific questions about it!"
(In theory we'd be better off not including the code every time but giving the assistant a tool that returns the current code)
It's understandably hard to not be implicitly biased towards talking to it in a natural way and expecting natural interactions and assumptions when the whole point of the experience is that the model talks in a natural language!
Luckily humans are intelligent too and the more you use this tool the more you'll figure out how to talk to it in a fruitful way.
That's easy. The answer is it doesn't. It has no understanding of anything it does.
> if it’s able to do it
This is the hard part.
