Why are LLMs general learners?
intuitiveai.substack.com
Why are LLMs general learners?
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Re: Why are LLMs general learners?
#2This seems like just another way of saying that when you train an LLM on a text, its weights incorporate the tokens in that text, which is nothing really profound.
I think the real magic here comes from the fact that LLMs are a specialized sort of neural network, and that neural networks are universal approximators [0]. In other words, LLMs are general learners because they are neural networks.
This is also not particularly profound, except that there are mathematical proofs of the universal approximation theorem that give us insight into why it must be so.
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[0]: https://en.wikipedia.org/wiki/Universal_approximation_theore...
Re: Why are LLMs general learners?
#3This seems like just another way of saying that when you train an LLM on a text, its weights incorporate the tokens in that text, which is nothing really profound. I think the real magic here comes from the fact that LLMs are a specialized sort of neural network, and that neural networks are universal approximators [0]. In other words, LLMs are general learners because they are neural networks. This is also not parti…
A lot of universal approximators are piss poor at general learning. It's taken a lot of hard work and clever people to get LLM's to where they are. It's not as simple as neural network and done.
Re: Why are LLMs general learners?
#4LLMs are not particularly good at arithmetic, counting syllables, or recognizing haikus, though, because (contrary to the thesis of the article) they don’t magically acquire whatever ability would “simplify” predicting the next token.
I don’t feel like the points made here align with any insight about the workings of LLMs. The fact that, as a human, I “wouldn’t know where to start” when asked to add two numbers without doing any addition doesn’t apply to computers (running predictive models). They would start with statistics over lots of similar examples in the training data. It’s still remarkable LLMs do so well on these problems, while at the same time doing somewhat poorly because they can’t do arithmetic!
Re: Why are LLMs general learners?
#5LLMs are not particularly good at arithmetic, counting syllables, or recognizing haikus, though, because (contrary to the thesis of the article) they don’t magically acquire whatever ability would “simplify” predicting the next token. I don’t feel like the points made here align with any insight about the workings of LLMs. The fact that, as a human, I “wouldn’t know where to start” when asked to add two numbers witho…
> LLMs are not particularly good at arithmetic, counting syllables, or recognizing haikus
I suspect most of this is due to tokenization making it difficult to generalize these concepts.
There are some weird edge cases though, for example GPT-4 will almost always be able to add two 40 digits number but it is also almost always wrong when adding a 40 digit and 35 digit number.
Re: Why are LLMs general learners?
#6> Yet, they demonstrate a crucial point: a deeper understanding of reality simplifies next-token prediction tasks.
I'm not sure LLMs are trained to simplify anything. They have billions of parameters after all.
Re: Why are LLMs general learners?
#7Intuitively, I think this also hints at why LLMs get more prone to confusion when trained to be "safe" - the underlying representations for applying human morality in context are much more complex to learn than simpler but potentially psychopathic logic.
Re: Why are LLMs general learners?
#8the main thing about LLM's in my opinion is the tokenization part, words are already clustered and converted into numbers(vectors) it's already a big deal. we are using learned weights,
the attention part feels like a brute force approach to learn how those vectors are likely used together (if you add positional encoding as an additional information).
statistics on large amount of amount of data just seems to work after all.
Re: Why are LLMs general learners?
#9LLMs are not particularly good at arithmetic, counting syllables, or recognizing haikus, though, because (contrary to the thesis of the article) they don’t magically acquire whatever ability would “simplify” predicting the next token. I don’t feel like the points made here align with any insight about the workings of LLMs. The fact that, as a human, I “wouldn’t know where to start” when asked to add two numbers witho…
> LLMs are not particularly good at arithmetic, counting syllables, or recognizing haikus I suspect most of this is due to tokenization making it difficult to generalize these concepts. There are some weird edge cases though, for example GPT-4 will almost always be able to add two 40 digits number but it is also almost always wrong when adding a 40 digit and 35 digit number.
It doesn't have anything to do with tokenization. You can define binary addition using symbols, e.g. a and b, and provide properly tokenized strings to GPT-4. GPT-4 appears to solve the arithmetic puzzles for a few bits, but quickly falls apart on larger examples.
Re: Why are LLMs general learners?
#10This seems like just another way of saying that when you train an LLM on a text, its weights incorporate the tokens in that text, which is nothing really profound. I think the real magic here comes from the fact that LLMs are a specialized sort of neural network, and that neural networks are universal approximators [0]. In other words, LLMs are general learners because they are neural networks. This is also not parti…
A lot of universal approximators are piss poor at general learning. It's taken a lot of hard work and clever people to get LLM's to where they are. It's not as simple as neural network and done.
Current LLM’s are also piss poor general learners, they are however really good at learning specific things which people value highly.