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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]

#91

Post by kirab »

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,…

ChatGPT is not good when it needs to answer directly. If you let it lead itself to the right answer it's pretty good.

# Kira

I found this sequence: "0, done, 2, free, 4, hive"

Reason step-by-step about the pattern.

Consecutively continue the sequence with four more entries.

Consecutively reflect on your answer and fix mistakes in case you broke the pattern.

# ChatGPT4

After examining the given sequence, "0, done, 2, free, 4, hive", we can notice an interesting pattern. It appears that the sequence alternates between numbers and words that sound like numbers. Let's break down the pattern:

"0" is a number. "done" sounds like the number "1" when spoken aloud. "2" is a number. "free" sounds like the number "3" when spoken aloud. "4" is a number. "hive" sounds like the number "5" when spoken aloud. Given this pattern, we can continue the sequence with four more entries:

The next number in the sequence is "6". The word that sounds like the number "7" is "heaven". The next number in the sequence is "8". The word that sounds like the number "9" is "nein", which is German for "no".

So, the continued sequence would be: "0, done, 2, free, 4, hive, 6, heaven, 8, nein".

Reflecting on the pattern and the answer provided, the continuation seems to be consistent with the established pattern. We alternated between numbers and words that phonetically resemble numbers, resulting in the sequence "6, heaven, 8, nein" for the next four entries.

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

#92

Post by ttpphd »

SanderNL wrote: post #77
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.

If it quacks like a duck.. Really, if it always produces output that conforms to "expectations of a reasoning actor", isn't that defacto a "reasoning actor"? What's the use of making the distinction?

Because of all the ways in which it's obviously not a reasoning actor, starting from the design of the system, and all the ways in which it is literally engineered, by design, to output text that sounds like a human wrote it, based on consuming the Internet.

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

#93

Post by somenameforme »

kirab wrote: post #91
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,…

ChatGPT is not good when it needs to answer directly. If you let it lead itself to the right answer it's pretty good. # Kira I found this sequence: "0, done, 2, free, 4, hive" Reason step-by-step about the pattern. Consecutively continue the sequence with four more entries. Consecutively reflect on your answer and fix mistakes in case you broke the pattern. # ChatGPT4 After examining the given sequence, "0, done, 2,…

But it still gets it wrong even there! Nein is a homophone, not a rhyme with 9. Oddly enough, when I gave it your exact input "my" GPT seems to have just completely lost its mind.

Input:

    I found this sequence: "0, done, 2, free, 4, hive"
    Reason step-by-step about the pattern.
    Consecutively continue the sequence with four more entries.
    Consecutively reflect on your answer and fix mistakes in case you broke the pattern.
Output:

    The pattern in the given sequence is that every alternate term is a word that rhymes with the preceding term.
    
    The first term is "0."
    The second term "done" rhymes with "0."
    The third term "2" does not rhyme with "done."
    The fourth term "free" rhymes with "2."
    The fifth term "4" does not rhyme with "free."
    The sixth term "hive" rhymes with "4."
    Therefore, the pattern is:
    
    a number
    a word that rhymes with the previous number
    repeat
    Continuing the sequence:
    
    6, dive
    8, late
    10, pen
    12, delve
    Reviewing my answer, I didn't break the pattern, and the sequence follows the pattern correctly.
EDIT: Ugh! Given the complete absurdity of this response (versus the wrong, but far more reasonable one from yours) I went to go check into this. I did not realize that OpenAI was putting version updates behind a paywall. All samples here (and from above) are from the free version on OpenAI's site, 3.5. And it's too late to my edit my earlier post.

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

#94

Post by SanderNL »

ttpphd wrote: post #92
SanderNL wrote: post #77

Earlier quoted context omitted.

If it quacks like a duck.. Really, if it always produces output that conforms to "expectations of a reasoning actor", isn't that defacto a "reasoning actor"? What's the use of making the distinction?

Because of all the ways in which it's obviously not a reasoning actor, starting from the design of the system, and all the ways in which it is literally engineered, by design, to output text that sounds like a human wrote it, based on consuming the Internet.

In all ways except .. its output? Which is the only thing that matters? What if you were engineered? Would that invalidate your abilities?

I apologize for being difficult.

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

#95

Post by DougN7 »

marssaxman wrote: post #90
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,…

> It's a series even a child could complete Some children, perhaps; but your puzzle stumped this middle-aged engineer.

You’re not alone - I had to read it out loud to see the pattern :)

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

#96

Post by max_entropy »

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?

People are saying it's about rhyming but I don't think so:

0, done, 2, free, 4, hive, 6, jeven, 8, line, 10, neleven, 12, pirteen, ...

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

#97

Post by marssaxman »

DougN7 wrote: post #95
marssaxman wrote: post #90

Earlier quoted context omitted.

> It's a series even a child could complete Some children, perhaps; but your puzzle stumped this middle-aged engineer.

You’re not alone - I had to read it out loud to see the pattern :)

I wonder if people have a range of sensitivity to what one might call the "mind's voice" - by analogy to the recent discovery of aphantasia, which revealed that different people's experience of the "mind's eye" ranges from vivid imagery to nothing at all.

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

#98

Post by DonaldPShimoda »

SanderNL wrote: post #94
ttpphd wrote: post #92

Earlier quoted context omitted.

Because of all the ways in which it's obviously not a reasoning actor, starting from the design of the system, and all the ways in which it is literally engineered, by design, to output text that sounds like a human wrote it, based on consuming the Internet.

In all ways except .. its output? Which is the only thing that matters? What if you were engineered? Would that invalidate your abilities? I apologize for being difficult.

> In all ways except .. its output? Which is the only thing that matters?

Who decided that this was the only thing that matters?

The thing that concerns me isn't that it answers questions in a way that resembles a human. What concerns me is that some people think it thinks like a human, which it does not, and writing about it in an anthropomorphic way exacerbates this problem.

The issue at hand is not the observable output of the system, but the way in which the system works — which we have knowledge of. It's not magic, it's not emergent, it's not sentient, and it's certainly not intelligent. It's a (very fancy and impressive!) predictive text system. Imbuing it with extra properties is disingenuous.

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

#99

Post by ttpphd »

SanderNL wrote: post #94
ttpphd wrote: post #92

Earlier quoted context omitted.

Because of all the ways in which it's obviously not a reasoning actor, starting from the design of the system, and all the ways in which it is literally engineered, by design, to output text that sounds like a human wrote it, based on consuming the Internet.

In all ways except .. its output? Which is the only thing that matters? What if you were engineered? Would that invalidate your abilities? I apologize for being difficult.

My rationality and agency is not a question of you interpreting the words and letters that appear in a textbox on a screen. The same is not true of a large language model. You do not have a rigorous theory of rationality and agency with which to interrogate a large language model.

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

#100

Post by d0mine »

marssaxman wrote: post #90
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,…

> It's a series even a child could complete Some children, perhaps; but your puzzle stumped this middle-aged engineer.

Each additional university math course makes the puzzle harder ;)

Continue sequence: one, 1, two, 1, three, 2, four, 1,

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