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These models are not trained on character level input. Why would anyone expect them to perform well on character level puzzles?
by kadushka 1y ago
These models are not trained on character level input. Why would anyone expect them to perform well on character level puzzles?
- Jensson 1y agoThey are trained on many billions of tokens of text dealing with character level input, they would be rather dumb if they couldn't learn it anyway. Every human learns that, when you hear the sound "strawberry" you don't hear the double r there, yet you still know the answer.
- brookst 1y agoThese models operate on tokens, not characters. It’s true that training budgets could be spent on exhaustively enumerating how many of each letter are in every word in every language, but it’s just not useful enough to be worth it. It’s more like asking a human for the Fourier components of how they pronounce “strawberry”. I mean the audio waves are right there, why don’t you know?
- yahoozoo 1y agoAlthough a vast majority of tokens are 4+ characters, you’re seriously saying that each individual character of the English alphabet didn’t make the cut? What about 0-9?
- kadushka 1y agoEach character made the cut, but the word "strawberry" is a single token, and that single token is what the model gets as input. When humans read some text, they can see each individual character in the word "strawberry" everytime they see that word. LLMs don't see individual characters when they process input text containing the word "strawberry". They can only learn the spelling if some text explicitly maps "strawberry" to the sequence of characters s t r a w b e r r y. My guess is there are not enough of such mappings present in the training dataset for the model to learn it well.
- nl 1y ago> the word "strawberry" is a single token, and that single token is what the model gets as input. This is incorrect. strawberry is actually 4 tokens (at least for GPT but most LLM are similar). See https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer
- kadushka 1y agoI got 3 tokens: st, raw, and berry. My point still stands: processing "berry" as a single token does not allow the model to learn its spelling directly, the way human readers do. It still has to rely on an explicit mapping of the word "berry" to b e r r y explained in some text in the training dataset. If that explanation is not present in the training data, it cannot learn the spelling - in principle.
- brookst 1y agoExactly. If “st” is 123, “raw” is 456, “berry” is 789, and “r” is 17… it makes little sense to ask the models to count the [17]’s in [123,466,789]: it demands an awareness of the abstraction that does not exist. To the extent the knowledge is there it’s from data in the input corpus, not direct examination of the text or tokens in the prompt.
- asadotzler 1y agoSo much for generalized intelligence, I guess.
- kadushka 1y agoIs a human who never learned how to read not generally intelligent?
- boroboro4 1y agoThe fact the word ends up being 1 token doesn’t mean model can’t track individual characters in it. The model transforms token into vector (of multiple thousands dimensionality), and I’m pretty sure there are dimensions corresponding to things like “if 1st character an ‘a’, 1st is ‘b’, 2nd is ‘a’ etc. So tokens aren’t as important.
- brrrrrm 1y agoemergent behavior. These things are surprisingly good at generalizing