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Eight things to know about large language models [pdf]

cims.nyu.edu

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Re: Eight things to know about large language models [pdf]

#51

Post by olalonde »

olivermuty wrote: post #46
olalonde wrote: post #41

Earlier quoted context omitted.

> It's still fairly exaggerated, because the LLM's purpose is basically "knowing human communication" and it still only exhibits that That's wrong. It's been demonstrated that GPT4 has a model of physics, it knows how to play chess, arithmetics, it can interpret and run code, etc. (not perfectly, but to some degree). It goes way beyond basically "knowing human communication".

Have you tried gpt4? Because I have and this sounds like wild cherrypicking of lucky results. «How to to play chess» -> devolve into illegal moves quickly? So does that mean that its model of physics will let it levitate as well then?

Yes, I use GPT4 daily[0].

> «How to to play chess» -> devolve into illegal moves quickly?

I didn't try it myself as I'm a beginner and need to see the board, but it reportedly plays pretty well and is able to recognize illegal moves[1].

> So does that mean that its model of physics will let it levitate as well then?

It means that it understands how physics work (e.g. things fall due to gravity). For example, it will correctly answer this question: "I put an egg inside a cup. I go to the bedroom, put the cup on the bed and turn it upside down. I lift the cup and bring it to the kitchen. Where is the egg?".

[0] Sample interactions: https://news.ycombinator.com/item?id=35299121

[1] https://www.reddit.com/r/chess/comments/11rfbch/gpt4_seems_t...

Re: Eight things to know about large language models [pdf]

#52

Post by PopePompus »

ttpphd wrote: post #37
cjbprime wrote: post #12

Earlier quoted context omitted.

> LLMs do not "reason"; they do not "learn" or "develop" anything of their own volition. If this claim of yours was sustainable, your grievance with the writing style would make a lot of sense. But GPT-4 can clearly reason about novel problems which were not in its training data. I think you should consider reading some of the many citations in the document which describe examples of that happening.

It doesn't reason, it produces output that better conforms to your expectations of a reasoning actor.

The models are showing emergent behavior. You can't say with certainty that they aren't able to reason, just because they were not designed to do so.

Re: Eight things to know about large language models [pdf]

#53

Post by ttpphd »

PopePompus wrote: post #52
ttpphd wrote: post #37

Earlier quoted context omitted.

It doesn't reason, it produces output that better conforms to your expectations of a reasoning actor.

The models are showing emergent behavior. You can't say with certainty that they aren't able to reason, just because they were not designed to do so.

I can confidently say that you do not have a theory of rationality which explains the workings of an LLM.

Re: Eight things to know about large language models [pdf]

#54

Post by PopePompus »

ttpphd wrote: post #53
PopePompus wrote: post #52

Earlier quoted context omitted.

The models are showing emergent behavior. You can't say with certainty that they aren't able to reason, just because they were not designed to do so.

I can confidently say that you do not have a theory of rationality which explains the workings of an LLM.

On that point we agree.

Re: Eight things to know about large language models [pdf]

#55

Post by TMWNN »

DonaldPShimoda wrote: post #3

This is a personal correspondence typeset via LaTeX — it is not an academic paper, and it was not peer-reviewed. (The document does not claim otherwise, but I think it's common for people to assume that documents that have been typeset in such a format are more rigorous than this is.) Leaving that aside, I really take issue with the style used by the author. For example, section 3 begins: > There is increasingly subs…

>LLMs do not "reason"; they do not "learn" or "develop" anything of their own volition. They are (advanced) statistical models.

As a non-expert in the field I was hesitant at the time to disagree with the legions of experts who last year denounced Blake Lemoine and his claims. I know enough to know, though, of the AI effect https://en.wikipedia.org/wiki/AI_effect>, a longstanding tradition/bad habit of advances being dismissed by those in the field itself as "not real AI". Anyone, expert or not, in 1950, 1960, or even 1970 who was told that before the turn of the century a computer would defeat the world chess champion would conclude that said feat must have come as part of a breakthrough in AGI. Same if told that by 2015 many people would have in their homes, and carry around in their pockets, devices that can respond to spoken queries on a variety of topics.

To put another way, I was hesitant to be as self-assuredly certain about how to define consciousness, intelligence, and sentience—and what it takes for them to emerge—as the experts who denounced Lemoine. The recent GPT breakthroughs have made me more so.

I found this recent Sabine Hossenfelder video interesting. https://www.youtube.com/watch?v=cP5zGh2fui0>

>Anthropomorphizing them is not just technically incorrect, but morally disingenuous.

Wat

Re: Eight things to know about large language models [pdf]

#56

Post by Al0neStar »

sdenton4 wrote: post #30
DonaldPShimoda wrote: post #15

Earlier quoted context omitted.

> Seriously just watch. He's not actually going to be able to coherently define his "reasoning" in a way that can be tested. Google gives the following definition of the verb "reason": > think, understand, and form judgments by a process of logic. LLMs do not think, they do not understand, and they do not form judgments. They do not come to their own conclusions. They do not have the physical capability. They are sta…

We're survival machines, nothing more... Complex behavior arises from simple systems all the time. You can't prove that these systems don't reason, no matter how loudly thou doth protest.

from the beginning of time people have been overestimating the complexity of things like the human brain and attributing it to magical things (like a creator) far beyond our comprehension but what seems to be happening now is that some people are underestimating it.

Re: Eight things to know about large language models [pdf]

#57

Post by somenameforme »

PopePompus wrote: post #45

I have a little weather station mounted outside my house, and there is a small linux machine (Raspberry Pi) logging the weather data into an SQLite database. I have almost two years of data now, so I asked GPT-4 to write a Python program that would query the database, calculate the average temperature for each day, fit a sine wave to those data points, find the first minimum of the sine wave and use that to calculate…

It's a word prediction algorithm. Literally any collection of words (sometimes also known as a fact) it was trained on, regardless of how esoteric or domain specific, should generally be able to be regurgitated and, to a lesser degree, associated with similar collections of words. If you want to see it fail, don't try to "outsmart" it, but simply consider how it's programmed.

---

Me: "continue the sequence: 0, done, 2, free, 4, hive"

ChatGPT 4: "1, sun, 3, tree, 5, drive"

Me: "that's wrong, try again"

ChatGPT 4: "0, done, 2, free, 4, hive, 6, picks, 8, gate"

Me: "that's wrong, try again"

ChatGPT 4: 'I give up.' (paraphrasing the obnoxiously verbose responses it's been trained to prefer)

---

It's a series even a child could complete, but breaks ChatGPT by relying on a pattern of words that aren't explicitly stated. So you get software which can pass the bar, but fail at trivial series.

Re: Eight things to know about large language models [pdf]

#58

Post by Kiro »

somenameforme wrote: post #57
PopePompus wrote: post #45

I have a little weather station mounted outside my house, and there is a small linux machine (Raspberry Pi) logging the weather data into an SQLite database. I have almost two years of data now, so I asked GPT-4 to write a Python program that would query the database, calculate the average temperature for each day, fit a sine wave to those data points, find the first minimum of the sine wave and use that to calculate…

It's a word prediction algorithm. Literally any collection of words (sometimes also known as a fact) it was trained on, regardless of how esoteric or domain specific, should generally be able to be regurgitated and, to a lesser degree, associated with similar collections of words. If you want to see it fail, don't try to "outsmart" it, but simply consider how it's programmed. --- Me: "continue the sequence: 0, done,…

I don't get it. What is the correct answer?

Re: Eight things to know about large language models [pdf]

#59

Post by i_cannot_hack »

Kiro wrote: post #58
somenameforme wrote: post #57

Earlier quoted context omitted.

It's a word prediction algorithm. Literally any collection of words (sometimes also known as a fact) it was trained on, regardless of how esoteric or domain specific, should generally be able to be regurgitated and, to a lesser degree, associated with similar collections of words. If you want to see it fail, don't try to "outsmart" it, but simply consider how it's programmed. --- Me: "continue the sequence: 0, done,…

I don't get it. What is the correct answer?

Every even item is the corresponding digit, every odd item is a word that rhymes with the corresponding digit.

A continuation could be "6, heaven, 8"

Re: Eight things to know about large language models [pdf]

#60

Post by [deleted] »

Kiro wrote: post #58
somenameforme wrote: post #57

Earlier quoted context omitted.

It's a word prediction algorithm. Literally any collection of words (sometimes also known as a fact) it was trained on, regardless of how esoteric or domain specific, should generally be able to be regurgitated and, to a lesser degree, associated with similar collections of words. If you want to see it fail, don't try to "outsmart" it, but simply consider how it's programmed. --- Me: "continue the sequence: 0, done,…

I don't get it. What is the correct answer?

[deleted]
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