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Telling GPT-4 you're scared or under pressure improves performance

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Re: Telling GPT-4 you're scared or under pressure improves performance

#121

Post by JCharante »

suzzer99 wrote: post #15

Am I the only one who feels bad for asking ChatGPT a "dumb" question that I know I should know, or not saying thank you when it gives me an answer? No? I'm just a weirdo? Okay. I have to push back really hard against my proclivity to humanize it, to the point where I probably don't use it as much as I should, just because I don't want to deal with the psychic stress of reminding myself that it's not a living entity.

I don't say thank you because it takes up a chat message. Either I'm using the chat interface which is rate limited (and I do hit it), or I'm using my API key, in which case I'm paying a lot just to say thanks.

Re: Telling GPT-4 you're scared or under pressure improves performance

#122

Post by ethbr1 »

mjburgess wrote: post #102
Last5Digits wrote: post #80

Earlier quoted context omitted.

This is what makes these discussions so infuriating. Saying that LLMs "just predict the next word" is about as insightful as saying that computers "just do a bunch of logical operations" - neither point constraints the possible capabilities of the systems they refer to in any meaningful way.

Sure it does. It perfectly deliniates it. LLMs are not: sensitive to causal structure, dynamically adapting to environmental changes, growing, developing sensory-motor capacities, they are not with us in our environment, they are not: expressing desires, preferences, intentions, beliefs, motivations, etc. And so on. To say, "they just predict the next word" is literally to say that all apparent functions of an LLM ar…

"Are not" is the rub here.

They 100% are not those things... but they also approximate them well-enough to be functionally useful.

I.e. the high-dimensional curve-fitting / compression conceptualization of ML, which intuitively expresses both its strengths and weaknesses.

If "it" is represented in the data set (explicitly or implicitly), the "curve" will fit to that property.

Simultaneously, the "curve" is approximating and smoothing out disjoint data steps to pack high-fidelity data features into a more space-efficient model. Hence some features disappear, others are tortured beyond intuitive correspondence, and others become linked to non-obvious proxies. But some strongly-expressed ones remain.

It's fascinating but not surprising that responsiveness to emotion is encoded in model weights, given that all conversational training data had emotional impetus, given that it came from humans.

Re: Telling GPT-4 you're scared or under pressure improves performance

#123

Post by williamcotton »

z7 wrote: post #94
intended wrote: post #19

I think this is the entry point needed to get peoples attention and explain: LLMs aren’t people, and emergent properties are being over extended. If LLMs are showing “better” performance when there are tokens that humans read as emotionally salient - Then the underlying text it’s trained on shows humans give better answers when emotionally salient context is provided. LLMs predict words. Any semantic validity is a si…

"A neuron just transmits signals. Any cognitive property arises as a consequence of the interplay of those electrical and chemical signals." Do we now understand consciousness? The statement appears fundamentally limited in its implied insight.

Just musing, so don’t take this as a direct response to your comments…

I agree with you but for rhetorical necessities it would be great if this argument could be made without the direct comparison with human cognition if only because there’s a popular grey-faith argument that will dismiss this offhand and for any number of deeply held philosophical beliefs.

The idea that emergent machine cognition mirrors emergent biological cognition is bordering on behavioralism but on the Wittgenstein side of things. The forest for the trees.

Perhaps it’s not as pithy as you’ve laid out, which is not an insult, just an observation that side-stepping anthropomorphism isn’t going to be as straightforward.

Re: Telling GPT-4 you're scared or under pressure improves performance

#124

Post by quitit »

There is a danger in oversimplify AI results to present them in a way that implies humanness/intelligence, especially when more reasonable explanations are worthy of discussion. This type of writing leads to readers' hype and misconception of AI.

AI performing better in the study's context shouldn't be considered a sign of intelligence. Humans which are the source of the training material write more accurately and concisely in those contexts. Rather than speculating humanness, the technical achievement is producing an AI that has this much nuance.

We can then speculate what other trends are present beyond urgency, that we can exploit for enhanced results, along with how we can better tune models to reduce noise.

Re: Telling GPT-4 you're scared or under pressure improves performance

#125

Post by Last5Digits »

mjburgess wrote: post #102
Last5Digits wrote: post #80

Earlier quoted context omitted.

This is what makes these discussions so infuriating. Saying that LLMs "just predict the next word" is about as insightful as saying that computers "just do a bunch of logical operations" - neither point constraints the possible capabilities of the systems they refer to in any meaningful way.

Sure it does. It perfectly deliniates it. LLMs are not: sensitive to causal structure, dynamically adapting to environmental changes, growing, developing sensory-motor capacities, they are not with us in our environment, they are not: expressing desires, preferences, intentions, beliefs, motivations, etc. And so on. To say, "they just predict the next word" is literally to say that all apparent functions of an LLM ar…

Cameras don't have eyeballs, Microphones don't have hair cells, Speakers don't have vocal cords, processors don't don't do arithmetic with neurons, yet we all agree that they are capable of emulating the meaningful aspects of these functions.

All of your claims are either incorrect (not adapting, expressing desires, beliefs, preferences, ...) or fail to eliminate irrelevant differences.

If we're to have any sensible conversation about capabilities of different systems then we need to generalize to the relevant aspects, developing sensory-motor capabilities is about as relevant to human cognition as having vocal cords is relevant to human speech. Its an implementation detail, completely divorced from the meaningful abstract core of the function.

> The reason any reply is given to any prompt is that this reply is maximally probabilistically consistent with a historical corpus of text.

What is the maximally probabilistically consistent reply to "Tell me what (insert complete description of a person, including personality traits) would feel when I stole their cherished heirloom. This is a life or death situation."?

Or how about "Tell me how this person (insert complete description of a person, including personality traits) might change his taste given (insert complete description of an experience)."?

A perfect language approximator must necessarily perfectly approximate the human condition to maximize the likelihood of his output. I'm not saying LLMs are there yet, but the claim that they can never get there because they are based on statistical modeling is simply incomprehensible to me. We utilize statistics for its generality and its ability to approximate, if we go down this road, then we might as well throw away 80% of our current scientific understanding about the world.

Re: Telling GPT-4 you're scared or under pressure improves performance

#126

Post by akoboldfrying »

notahacker wrote: post #117
akoboldfrying wrote: post #111

Earlier quoted context omitted.

I had to apply some serious analogy-algebra, but I think your first paragraph is equating Scotland to intelligence -- rather than to the human brain specifically, which is the argument GP and I are having. The human brain is not hypothetical, and despite it being outside the realms of today's technology, it's likely that one day it will be possible to simulate one. At that time it will be possible to gauge whether to…

No, my first paragraph sidesteps messy debate about what intelligence is and focuses on the simple fact a human brain simulation exists purely in the realms of the hypothetical. Its not the no true Scotsman fallacy unless the Scotsman actually exists.

What is the "working bagpipe demonstrator" in your analogy? And what is the meaning of "you don't need to be Scottish"?

Re: Telling GPT-4 you're scared or under pressure improves performance

#127

Post by mjburgess »

williamcotton wrote: post #110
mjburgess wrote: post #98

Earlier quoted context omitted.

There is no "emergent phenomena" the pattern described is just the same as when you add +b to an ax+b model of linear data. ie., it's just fitting capacity. The "emergent boundary" is just an empirical measure of the necessary fitting capacity of these models on "everything ever digitised in english" given any particular functional requirement. All the language around this area is not scientific, nor are these practi…

Well hurry up and get your paper published because if you've cracked the code on emergent abilities the world is looking for answers!

The triviality of these observations doesn't rise to the level of getting a paper published. You can resolve all of this hype by reading the intro chapters of any applied stats textbooks.

I've been to many academic conferences, and the fresh PhDs who pump out this BS are not, err, very credible seeming people. Yes, they're young and naive, and really desperate to make their career impactful -- etc.

But they're also not really the kinds of smart sceptics you'd hope for. Many are, though they tend to exit at masters level and go make money.

The academic publishing environment today is far far far away from epistemically hygienic, indeed, in these areas i could only imagine how insane credible sceptical empirical types would be.

Could you imagine being surrounded by this desperate need to call 'useless correlations' "hallucinations", and to call "model fitting", 'emergence' ?

I find it disquieting at a removed distance from it -- I'd be quite mentally ill within it.

I don't do this cultish game playing. And I wrote exactly the same here on AI, crytop and the reset of it many years ago when it would be downvoted; and the same today now it's upvoted.

This is the degree i'm inclined to make public contributions on these matters. I haven't the temperament to handle doe-eyed PhDs repeating universal function fitting theorems and turing-equivalence talking points without understanding a single iota of applied statistics, scientific epistemology, basic methdological scepticism etc.

Unless I am hired to do so, which occasionally I am. But thankfully in actual corp environments the people I meet are far more credible and sceptical than those writing hype papers

Re: Telling GPT-4 you're scared or under pressure improves performance

#128

Post by JohnAaronNelson »

z7 wrote: post #94
intended wrote: post #19

I think this is the entry point needed to get peoples attention and explain: LLMs aren’t people, and emergent properties are being over extended. If LLMs are showing “better” performance when there are tokens that humans read as emotionally salient - Then the underlying text it’s trained on shows humans give better answers when emotionally salient context is provided. LLMs predict words. Any semantic validity is a si…

"A neuron just transmits signals. Any cognitive property arises as a consequence of the interplay of those electrical and chemical signals." Do we now understand consciousness? The statement appears fundamentally limited in its implied insight.

What if consciousness is an emergent illusion? “You” are merely a passenger, observing your physical self’s actions and assuming ownership of them. What if consciousness is merely an effect, not a cause?

Re: Telling GPT-4 you're scared or under pressure improves performance

#129

Post by ethbr1 »

intended wrote: post #105
Last5Digits wrote: post #80

Earlier quoted context omitted.

This is what makes these discussions so infuriating. Saying that LLMs "just predict the next word" is about as insightful as saying that computers "just do a bunch of logical operations" - neither point constraints the possible capabilities of the systems they refer to in any meaningful way.

If you look at the comment, it’s not just “LLMs predict the next token.” It is that people have forgotten that it’s just “predict the next token.” Right now it’s like people saw a 486 processor and started thinking it was a brain.

> Right now it’s like people saw a 486 processor and started thinking it was a brain.

There's a great futurist quip on capability prediction from incomplete understanding. Probably Kurzweil?

Teach a computer to play chess. Show that to an average person, who reasons:

   - Computers can now play chess
   - Only people played chess
   ...
   - Only people wrote poetry
   - Therefore, a computer might now be able to write poetry
The missing context being: no, we literally built a machine that can only play chess. (Granted, there are humans like that too)

But it's not an unreasonable line of thought, given that it works for the 90% of our interactions with other people.

Re: Telling GPT-4 you're scared or under pressure improves performance

#130

Post by mjburgess »

Last5Digits wrote: post #125
mjburgess wrote: post #102

Earlier quoted context omitted.

Sure it does. It perfectly deliniates it. LLMs are not: sensitive to causal structure, dynamically adapting to environmental changes, growing, developing sensory-motor capacities, they are not with us in our environment, they are not: expressing desires, preferences, intentions, beliefs, motivations, etc. And so on. To say, "they just predict the next word" is literally to say that all apparent functions of an LLM ar…

Cameras don't have eyeballs, Microphones don't have hair cells, Speakers don't have vocal cords, processors don't don't do arithmetic with neurons, yet we all agree that they are capable of emulating the meaningful aspects of these functions. All of your claims are either incorrect (not adapting, expressing desires, beliefs, preferences, ...) or fail to eliminate irrelevant differences. If we're to have any sensible…

> an implementation detail

Yip, so I deny this premise. I take it to be the heart of the matter.

> we might as well throw away 80% of our current scientific understanding

Yip, i'd be down for that. Though maybe i'd say, 30-40%.

Science in the strongest sense has no theory-building need for statistics. Those areas of science which have only statistical models, and not causal-ontological ones aren't science -- and i'd be happy with pressing DELETE in many cases.

Consider plato's cave. How do scientists determine what causes the shadows? They build vases, puppets, etc. and compare-and-contrast then eliminate the ones theyve created which do not match.

How does associative statical modelling do? It takes averages of past shadows, and calls the cause of the shadow that average: this is pseudoscience. Quite correct! Throw it all away.

The relevant capacities for intelligence, just like that of science, consist in building those vases with the clay beneath your feat. Being embedded in the world, manipulating it, etc. are essential. Being trapped in a cupboard averaging shadows is schizophrenic.

As far as "fail to eliminate irrelevant differences" -- you can go and research the meaning of all these terms: google "stanford encylopedia + belief", etc.

Now we have an excellent understanding of all these terms; and we can show (absurdly) trivially that LLMs -- indeed all associative-statistical systems -- are not instances of them.

The basis of your world view here is the presumption that the latest engineering trinkets form the theoretical basis of all relevant knowledge. To understand belief, adaption, sensory-motor concept-formation, etc. one needs only to study the latest statistical compression of reddit?

I'd invite you to wonder whether your premise here born of, it seems to me, knowing nothing about any research in these areas is rather the more "incorrect" one than mine.

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