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LLMs understand nullability

dmodel.ai

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Re: LLMs understand nullability

#11

Post by throwuxiytayq »

fallingknife wrote: post #8
EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

Or like saying the photoreceptors in your retina understand when it's dark. Or like claiming the temperature sensitive ion channels in your peripheral nervous system understand how hot it is.

Or like saying that the tangled web of neurons receiving signals from these understands anything about these subjects.

Re: LLMs understand nullability

#12

Post by dleeftink »

EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

I'd say the opposite also applies: to the extent LLMs have an internal language, we understand very little of it.

Re: LLMs understand nullability

#13

Post by dfilppi »

EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

[dead]

Re: LLMs understand nullability

#14

Post by mubou »

robotresearcher wrote: post #7
EncomLab wrote: post #2

This is like claiming a photorestor controlled night light "understands when it is dark" or that a bimetallic strip thermostat "understands temperature". You can say those words, and it's syntactically correct but entirely incorrect semantically.

You declare this very plainly without evidence or argument, but this is an age-old controversial issue. It’s not self-evident to everyone, including philosophers.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works.

Any comparison to the human brain is missing the point that an LLM only simulates one small part, and that's notably not the frontal lobe. That's required for intelligence, reasoning, self-awareness, etc.

So, no, it's not a question of philosophy. For an AI to enter that realm, it would need to be more than just an LLM with some bells and whistles; an LLM plus something else, perhaps, something fundamentally different which does not yet currently exist.

Re: LLMs understand nullability

#15

Post by jayd16 »

gopiandcode wrote: post #5

The visualisation of how the model sees nullability was fascinating. I'm curious if this probing of nullability could be composed with other LLM/ML-based python-typing tools to improve their accuracy. Maybe even focusing on interfaces such as nullability rather than precise types would work better with a duck-typed language like python than inferring types directly (i.e we don't really care if a variable is an int sp…

Why not just use a language with checked nullability? What's the point of an LLM using a duck typing language anyway?

Re: LLMs understand nullability

#16

Post by aSanchezStern »

nonameiguess wrote: post #10

As every fifth thread becomes some discussion of LLM capabilities, I think we need to shift the way we talk about this to be less like how we talk about software and more like how we talk about people. "LLM" is a valid category of thing in the world, but it's not a thing like Microsoft Outlook that has well-defined capabilities and limitations. It's frustrating reading these discussions that constantly devolve into o…

Yeah the link title is overclaiming a bit, the actual post title doesn't make such a general claim, and the post itself examines several specific models and compares their understanding.

Re: LLMs understand nullability

#17

Post by aSanchezStern »

mubou wrote: post #14
robotresearcher wrote: post #7

Earlier quoted context omitted.

You declare this very plainly without evidence or argument, but this is an age-old controversial issue. It’s not self-evident to everyone, including philosophers.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

Many people don't think we have any good evidence that our brains aren't essentially the same thing: a stochastic statistical model that produces outputs based on inputs.

Re: LLMs understand nullability

#18

Post by aSanchezStern »

jayd16 wrote: post #15
gopiandcode wrote: post #5

The visualisation of how the model sees nullability was fascinating. I'm curious if this probing of nullability could be composed with other LLM/ML-based python-typing tools to improve their accuracy. Maybe even focusing on interfaces such as nullability rather than precise types would work better with a duck-typed language like python than inferring types directly (i.e we don't really care if a variable is an int sp…

Why not just use a language with checked nullability? What's the point of an LLM using a duck typing language anyway?

This post actually mostly uses the subset of Python where nullability is checked. The point is not to introduce new LLM capabilities, but to understand more about how existing LLMs are reasoning about code.

Re: LLMs understand nullability

#19

Post by SJC_Hacker »

aSanchezStern wrote: post #17
mubou wrote: post #14

Earlier quoted context omitted.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

Many people don't think we have any good evidence that our brains aren't essentially the same thing: a stochastic statistical model that produces outputs based on inputs.

Thats probably the case 99% of the time.

But that 1% is pretty important.

For example, they are dismal at math problems that aren't just slight variations of problems they've seen before.

Here's one by blackandredpenn where ChatGPT insisted the solution to problem that could be solved by high school / talented middle school students was correct, even after trying to convince it it was wrong. https://youtu.be/V0jhP7giYVY?si=sDE2a4w7WpNwp6zU&t=837

Rewind earlier to see the real answer

Re: LLMs understand nullability

#20

Post by gwd »

mubou wrote: post #14
robotresearcher wrote: post #7

Earlier quoted context omitted.

You declare this very plainly without evidence or argument, but this is an age-old controversial issue. It’s not self-evident to everyone, including philosophers.

It's not age-old nor is it controversial. LLMs aren't intelligent by any stretch of the imagination. Each word/token is chosen as that which is statistically most likely to follow the previous. There is no capability for understanding in the design of an LLM. It's not a matter of opinion; this just isn't how an LLM works. Any comparison to the human brain is missing the point that an LLM only simulates one small part…

> Each word/token is chosen as that which is statistically most likely to follow the previous.

The best way to predict the weather is to have a model which approximates the weather. The best way to predict the results of a physics simulation is to have a model which approximates the physical bodies in question. The best way to predict what word a human is going to write next is to have a model that approximates human thought.

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