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Talking About Large Language Models

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Re: Talking About Large Language Models

#41

Post by CrypticShift »

> sudden presence among us of exotic, mind-like entities might precipitate a shift in the way we use familiar psychological terms ... But it takes time for new language to settle, and for new ways of talking to find their place in human affairs ... Meanwhile, we should try to resist the siren call of anthropomorphism.

Yes: Human analogies are not very useful because they create more misunderstanding than they dissipate. Dumb ? Conscious ? No thanks. IMO even the “i” in “AI” was already a (THE ?) wrong choice. They thought we will soon figure out what Intelligence is. Nope. Bad luck. And this "way of talking" (and thinking) is unfortunately cemented today.

However, I'm all for using other analogies more often. We need to. They may not be precise, but if they are well-chosen, they speak to us better than any technical jargon (LLM anyone ?), better than that “AI” term itself anyway.

Here is two I like (and never see much) :

- LLMs are like the Matrix (yes that one !), in the straightforward sense that they simulate reality (through language). But that simulation is distorted and sometimes even verges on the dream ("what is real? what is not?", says the machine)

- LLMs are like complex systems [1]. They are tapping into very powerful natural processes where (high degree) order emerges from randomness through complexity. We are witnessing the emergence of a new kind of "entity" in a way strangely akin to natural/physical evolutionary mechanisms.

We need to get more creative here and stop that boring smart VS dumb or human VS machine ping pong game.

[1] https://en.wikipedia.org/wiki/Complex_system

Re: Talking About Large Language Models

#42

Post by b3morales »

TeMPOraL wrote: post #24
nathan_compton wrote: post #16

Earlier quoted context omitted.

Yeah, but its not as reliable as just restricting your search to Wikipedia or the appropriate academic journals or even chatting with a librarian!

Sure, when the topic matters or I need to study it in depth, I can still go to Wikipedia or PubMed or Arxiv. But there are plenty of searches one does that are trivial, or serve to illuminate the problem space, and cover topics that in which I can rely on common sense to correct wrong advice. And the issue with non-technical topics, the kind applicable to mass audience - like e.g. cooking or parenting or hygiene - ar…

But if so this isn't because of its nature (the fact that it's an LLM), but because of its inputs. An LLM fed the same bullshit pseudo articles you refer to would likewise spit out more bullshit. If ChatGPT works it's because its sources have been carefully curated.

Re: Talking About Large Language Models

#43

Post by Al-Khwarizmi »

TeMPOraL wrote: post #24
nathan_compton wrote: post #16

Earlier quoted context omitted.

Yeah, but its not as reliable as just restricting your search to Wikipedia or the appropriate academic journals or even chatting with a librarian!

Sure, when the topic matters or I need to study it in depth, I can still go to Wikipedia or PubMed or Arxiv. But there are plenty of searches one does that are trivial, or serve to illuminate the problem space, and cover topics that in which I can rely on common sense to correct wrong advice. And the issue with non-technical topics, the kind applicable to mass audience - like e.g. cooking or parenting or hygiene - ar…

Indeed. If I could have the Google from 20 years ago, I probably wouldn't be so impressed with ChatGPT as search engine.

But with the Google (and the web) of today, where it's practically impossible to find reliable information about many subjects without adding "site:reddit.com" or "wikipedia", I find it extremely useful.

Re: Talking About Large Language Models

#44

Post by radford-neal »

marcinzm wrote: post #34
nathan_compton wrote: post #15

Earlier quoted context omitted.

I've got a young kid and I'd think twice before letting this model explain any science to him. If your criteria for whether a model is good is "it fooled a 9th grader" well, I don't know what to tell you. I think you have a point about your tkinter example. That kind of stuff _is_ a lot more convenient than googling and copying and pasting code. But if you push it beyond stuff that you could easily find on stack exch…

> If your criteria for whether a model is good is "it fooled a 9th grader" well, I don't know what to tell you. Fooling a 9th grader is amazing. That's a pretty well formed human being right there except with less life experience. Fundamentally no different from you in general reasoning terms except on a smaller set of information. So fooling you is merely a question of model size.

"Fool" is the operative word here. ChatGPT is quite capable of producing very plausible sounding text about biology that is totally incorrect. See, for example, the example in my comment at https://www.lesswrong.com/posts/28XBkxauWQAMZeXiF/?commentId...

Re: Talking About Large Language Models

#45

Post by goatlover »

axg11 wrote: post #9
nathan_compton wrote: post #2

This will hardly seem like a controversial opinion, but LLM are overhyped. Its certainly impressive to see the things people do with them, but they seem pretty cherry-picked to me. When I sat down with ChatGPT for a day to see if it could help me with literally any project I'm currently actually interested in doing it mostly failed or took so much prompting and fiddling that I'd rather have just written the code or d…

I disagree that LLMs are overhyped, but it's very subjective. Are current LLMs a few steps from AGI? No. Will LLMs change the computing landscape? Yes, I believe they will. ChatGPT, without any major changes, is already the best tool out there for answering programming questions. Nothing else comes close. I can ask it to provide code for combining two APIs and it will give useful and clean output. No need to trudge t…

One concern here is that if ChatGPT replaces the need to go to websites like Stack Overflow or Wikipedia, what happens to them? Do they stick around if the only people who visit them are there to feed new stuff to chatGPT? Also, how does chatGPT get hold of papers and articles behind pay walls? How much of the scientific publications are free?

Re: Talking About Large Language Models

#46

Post by RosanaAnaDana »

anyonecancode wrote: post #29
RosanaAnaDana wrote: post #11

I like the discussion, but this article 'feels' like more Luddite goalpost moving, and is reflective of a continuous sentiment I feel strains so much of the conversation around intelligence, agentism, and ai going on today. I think that because we lack a coherent understanding of what it means to be intelligent at an individual level, as well as what it means to be an individual, we're missing much of the point of wh…

Increasingly I don't think the question of "what is intelligence" is so useful or relevant here. It feels a bit like arguing over whether the "artificial horse" that started appearing at the end of the 19th/beginning of the 20th C were actually horses. Cars weren't, and still aren't, but that misses the point. AI isn't intelligent, and never will be, and I don't think that matters all that much.

I think I agree in sentiment, and I'm wondering what your ake is on the article/ current discussions article.

I guess my premise is that I don't think we have a useful enough definition of intelligence because the ones I see people writing articles on seem to be dependent or defined by agency, and specifically humanish forms of agency. So I guess your point would be "these systems aren't intelligent, but that's not relevant"? I suppose I out the issue at the currency of the definition of intelligence. It's seemed to be very much synonymous with "how humans do things", making it somewhat impossible to give charity to the arguments presented in this paper with the caveats on "not anthropomorphising". Like I can't compare these two things if your definition of intelligence is fundementally based on what "Anthros" do or do not do and simultaneously not engage in anthropromorism.

To follow on your point, if these things aren't displaying "intelligence", but that's also not the point, what then are they displaying?

It seems to me this is a failure of introspection on the part of AI philosophy to recognize how limited our understanding of "HI" is.

Re: Talking About Large Language Models

#47

Post by Al-Khwarizmi »

nathan_compton wrote: post #2

This will hardly seem like a controversial opinion, but LLM are overhyped. Its certainly impressive to see the things people do with them, but they seem pretty cherry-picked to me. When I sat down with ChatGPT for a day to see if it could help me with literally any project I'm currently actually interested in doing it mostly failed or took so much prompting and fiddling that I'd rather have just written the code or d…

Do my core work? No, it's not going to, at the moment.

But it's already saving me nontrivial amounts of time on tasks like "write a polite followup email reminding person X, who didn't reply to the email I sent last week, that the deadline for doing Y expires at date Z".

I typically spend at least 3-4 minutes finding the words for such a trivial email and thinking how to write it best, e.g. trying to make the other person react without coming across as annoying, etc. (Being a non-native English speaker who communicates mostly in English at work may be a factor). ChatGPT is really good with words. Using it, it takes a few seconds and I can use the output with only trivial edits.

Re: Talking About Large Language Models

#48

Post by albystein »

gamegoblin wrote: post #5

Everyone pointing out how LLMs fail at some relatively simple tasks are fundamentally misunderstanding the utility of LLMs. Don't think of an LLM as a full "computer" or "brain". Think of it like a CPU. Your CPU can't run whole programs, it runs single instructions. The rest of the computer built around the CPU gives it the ability to run programs. Think of the LLM like a neural CPU whose instructions are relatively…

This a very well put comment with a great analogy. A new emerging paradigm of action-driven LLMs is taking the approach of using the reasoning abilities of LLMs to drive agents that can take actions, interact with other tools and computer programs, and perform useful tasks like autonomously programming, customer support, etc

And I think you’re right when you say that they’re lacking in recursive thinking abilities. However, their reasoning abilities are pretty excellent which is why when you prompt them to think step-by-step, or break down problems to them, they correctly output the right answer.

Re: Talking About Large Language Models

#49

Post by goatlover »

canjobear wrote: post #18

I’ll agree to stop saying LM’s “think” and “know” things if you can tell me precisely what those mean for humans.

Maybe there isn't a precise definition, but clearly for humans thinking and knowing is related to having bodies that need to survive in the world with other humans and organisms, which involves communication and references to external and internal things (how your body feels and what not). This is different from pattern matching tokens, even if it reproduces a lot of the same results, because human language creates a lot of patterns that can be matched.

We could say both humans and LLMs are intelligent, but in a different way.

Re: Talking About Large Language Models

#50

Post by dragonwriter »

TeMPOraL wrote: post #25
dragonwriter wrote: post #22

Earlier quoted context omitted.

> Under what circumstances do LLMs give answers that are reliably equivalent to or better than humans? Which humans? Humans give a... fairly wide range of responses.

Average humans? Within 1 stdev from the mean?

> Average humans? Within 1 stdev from the mean?

This implies that performance has unqiue natural, objective, ratio-level (or at least, a unique consistent interval-level) measure. Otherwise the mean is, itself, meaningless.

“How well you answer a question” doesn't seem to fit that, its maybe at best a (somewhat subjective, still) ordinal quality, so the median (or mode) is the only useful average.

But I think you'll find that without restricting things more than “humans”, both the median and mode of responses to most prompts is... quite bad.

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