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Do Machine Learning Models Memorize or Generalize?

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Re: Do Machine Learning Models Memorize or Generalize?

#31

Post by famouswaffles »

Salgat wrote: post #23
visarga wrote: post #15

Earlier quoted context omitted.

> I just don't think enough attention has been paid to the data, and too much the model. I wholly agree. Everyone is blinded by models - GPT4 this, LLaMA2 that - but the real source of the smarts is in the dataset. Why would any model, no matter how its architecture is tweaked, learn about the same ability from the same data? Why would humans be all able to learn the same skills when every brain is quite different. I…

This is definitely current models' biggest issue. You're training a model against millions of books worth of data (which would take a human tens of thousands of lifetimes) to achieve a superficial level of conversational ability to match a human, which can consume at most 3 novels a day without compromising comprehension. Current models are terribly inefficient when it comes to learning from data.

Modern LLMs are nowhere near the scale of the human brain however you want to slice things so terribly inefficient is very arguable. also language skills seemingly take much less data and scale when you aren't trying to have it learn the sum total of human knowledge. https://arxiv.org/abs/2305.07759

Re: Do Machine Learning Models Memorize or Generalize?

#32

Post by taeric »

jaggirs wrote: post #29
taeric wrote: post #17

I'm curious how representative the target function is? I get that it is common for you to want a model to learn the important pieces of an input, but a string of bits, and only caring about the first three, feels particularly contrived. Literally a truth table on relevant parameters of size 8? And trained with 4.8 million samples? Or am I misunderstanding something there? (I fully expect I'm misunderstanding somethin…

I have observed this pattern before in computer vision tasks (train accuracy flatlining for a while before test acc starts to go up). The point of the simple tasks is to be able to interpret what could be going on behind the scenes when this happens.

No doubt. But I have also seen what people thought were generalized models failing on outlier, but valid, data. Quite often.

Put another way, it isn't just how simple this task seems to be in the number of terms that are important, but isn't it also a rather dense function?

Probably better question to ask is how sensitive are models that are looking at less dense functions to this? (Or more dense.). I'm not trying to disavow the ideas.

Re: Do Machine Learning Models Memorize or Generalize?

#33

Post by paulddraper »

gorjusborg wrote: post #22

Grr, the AI folks are ruining the term 'grok'. It means roughly 'to understand completely, fully'. To use the same term to describe generalization... just shows you didn't grok grokking.

What the difference between understanding and generalizing?

And what is the indicator for a machine understanding something?

Re: Do Machine Learning Models Memorize or Generalize?

#34

Post by [deleted] »

greenflag wrote: post #5

It seems the take home is weight decay induces sparsity which helps learn the "true" representation rather than an overfit one. It's interesting the human brain has a comparable mechanism prevalent in development [1]. I would love to know from someone in the field if this was the inspiration for weight decay (or presumably just the more equivalent nn pruning [2]). [1] https://en.wikipedia.org/wiki/Synaptic_pruning [2…

[deleted]

Re: Do Machine Learning Models Memorize or Generalize?

#35

Post by gorjusborg »

NikkiA wrote: post #27
gorjusborg wrote: post #22

Grr, the AI folks are ruining the term 'grok'. It means roughly 'to understand completely, fully'. To use the same term to describe generalization... just shows you didn't grok grokking.

I've always taken 'grok' to be in the same sense as 'to be one with'

Yeah, there is definitely irony that I'm trying to push my own definition of an extra-terrestrial word, complaining that someone is ruining it.

If anyone wants to come up with their own definition, read Robert Heinlein's 'Stranger in a Strange Land'. There is no definition in there, but you build an intuition of the meaning by its use.

One of the issues I have w/ the use in AI is that using the word 'grok' suggests that the machine understands (that's a common interpretation of the word grok, that it is an understanding greater than normal understanding).

By using an alien word, we are both suggesting something that probably isn't technically true, while simultaneously giving ourselves a slimy out. If you are going to suggest that AI understands, just have the courage to say it with common english, and be ready for argument.

Redefining a word that already exists to make the argument technical feels dishonest.

Re: Do Machine Learning Models Memorize or Generalize?

#36

Post by mxwsn »

gorjusborg wrote: post #22

Grr, the AI folks are ruining the term 'grok'. It means roughly 'to understand completely, fully'. To use the same term to describe generalization... just shows you didn't grok grokking.

They're just defining grokking in a different way. It's reasonable to me though - grokking suggests elements of intuitive understanding, and a sudden, large increase in understanding. These mirror what happens to the loss.

Re: Do Machine Learning Models Memorize or Generalize?

#37

Post by mostertoaster »

Sometimes I think the reason human memory in some sense is so amazing, is what we lack in storage capacity that machines have, we makeup for in our ability to create patterns that compress the amount of information stored dramatically, and then it is like we compress those patterns together with other patterns and are able to extract things from it. Like it is an incredibly lossy compression, but it gets the job done.

Re: Do Machine Learning Models Memorize or Generalize?

#38

Post by gorjusborg »

erwald wrote: post #24
gorjusborg wrote: post #22

Grr, the AI folks are ruining the term 'grok'. It means roughly 'to understand completely, fully'. To use the same term to describe generalization... just shows you didn't grok grokking.

"Grok" in AI doesn't quite describe generalization, it's more specific that that. It's more like "delayed and fairly sudden generalization" or something like that. There was some discussion of this in the comments of this post[1], which proposes calling the phenomenon "eventual recovery from overfitting" instead. [1] https://www.lesswrong.com/posts/GpSzShaaf8po4rcmA/qapr-5-gro...

Whoever suggested 'eventual recovery from overfitting' is a kindred spirit.

Why throw away the context and nuance?

That decision only further leans into the 'AI is magic' attitude.

Re: Do Machine Learning Models Memorize or Generalize?

#39

Post by momirlan »

gorjusborg wrote: post #22

Grr, the AI folks are ruining the term 'grok'. It means roughly 'to understand completely, fully'. To use the same term to describe generalization... just shows you didn't grok grokking.

grok, implying a mystical union, is not applicable to AI
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