Comment by 2snakes
1 day ago
I read one characterization which is that LLMs don't give new information (except to the user learning) but they reorganize old information.
1 day ago
I read one characterization which is that LLMs don't give new information (except to the user learning) but they reorganize old information.
Custodians of human knowledge.
That’s only true if you tokenize words rather than characters. Character tokenization generates new content outside the training vocabulary.
All major tokenisers have explicit support for encoding arbitrary byte sequences. There's usually a consecutive range of tokens reserved for 0x00 to 0xFF, and you can encode any novel UTF-8 words or structures with it. Including emoji and characters that weren't a part of the model's initial training, if you show it some examples.
Pretty sure that we’re talking apples and oranges. Yes to the arbitrary byte sequences used by tokenizers, but that is not the topic of discussion. The question is will the tokenizer come up with words not in the training vocabulary. Word tokenizers don’t, but character tokenizers do.
Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.
“If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”
"If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."
Why stop there? Just have it spit out the state of the bits on the hardware. English seems like a serious shackle for an LLM.
Kind of, but character-based tokens make it a lot harder and more expensive to learn semantics.
Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.
“If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”
"If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."