4 ms·

Not sure what you mean here—care to elaborate? The eventual input to these models are integer token IDs (128k different ones for DeepSeek). The tokenizers do th

by pama 2y ago

Not sure what you mean here—care to elaborate? The eventual input to these models are integer token IDs (128k different ones for DeepSeek). The tokenizers do the conversions from Unicode streams to streams of token IDs.

cchance 2y ago
I still wonder why we're training models on all these languages especially when they have different alphabets etc, we've got solid translators, wouldn't it be more parameter dense to target one language for all data and tokens, and then have a layer specifically for input and output translation?
- eightysixfour 2y ago
  I'd be interested to know if adding more languages makes them more or less performant. It is my understanding that you have to add code for the models to perform well, for example.
  - cchance 2y ago
    My issue and questions isn't so much languages but character sets, expanding say an ascii dataset to include all the non-ascii language glyphs or other unicode characters has got to balloon the parameter count
- ijustlovemath 2y ago
  more languages gives deeper semantic understanding; I think it only helps with diversity of data, which ultimately improves outputs
- sva_ 2y ago
  You'll have a lower bound on the quality of the translator you're using. There's an idea that you can generalize concepts among different languages, and that you'll benefit from the extended training corpus. As in, talking about an idea from different perspectives helps the model carve it out. But I don't have anything concrete to back that claim up.
  - econ 2y ago
    Each language has unique tools. If you have a word for something or even better a whole set of words the conversation works a lot better than in a language that has nothing of the kind. English elaborately talks about all kinds of Communities. Dutch does have a word like it but it is almost never used. Or, how should we talk about the kind of snow if we have only one word? https://watchingtheswedes.com/2018/02/28/50-words-for-snow/ https://watchingtheswedes.com/2018/02/28/50-words-for-snow/
- cma 2y ago
  More languages helps it at novel translation tasks, models have been tested with languages not in/barely in the corpus and a translation book in context and were able to do an ok job. You'll also have things like mulimodal where you want to preserve all the tonality and emphasis in the input language.
- astrange 2y ago
  Bitter lesson says any kind of specialization is not worth it[0]. Also, you want to be able to have mixed language conversations, like defining a Chinese word in English. [0] but it might be worth it if you need a smaller model because then there are tradeoffs again.
amluto 2y ago
From the OP, it sounds like those tokens are generated from the UTF-8 bytes instead of from the Unicode code points. And those bytes are, taken in isolation, complete nonsense. Imagine a token that represented the right side of the letter d followed by the left side of the letter e but could also represent other mishmashes of characters. I bet the first layer of the model is mostly stuck reconstructing something resembling actual words. (UTF-8 is locally decidable. I bet that a bit of work on the token list could cause it to avoid tokens that do not align with code point boundaries.)
- pama 2y ago
  To be clear these tokenizers use byte-pair encoding (subword tokens) so an individual token index typically corresponds to a piece of a word; this index does not depend on any intermediate decoding of the byte stream as long as the start of the stream is a start of your input. The decoding always works left to right and always starts at the start of the stream. You could write a tokenizer that uses plain bytes and one that uses unicode code points if your tokenizer was trained on unicode and forced to keep unicode codes together (almost all are), and the results would be identical for all practical purposes.
  - mmoskal 2y ago
    llama2, llama3, gpt3, and gpt4 tokenizers are all trained on UTF8 bytes and all include invalid (partial) UTF8 tokens. For llama3 it's only 256 tokens, one for each byte, but for the others it's more interesting (eg., 1361 tokens with UTF8 fragments in llama3). There is over 1M possible Unicode code points, and 150k actually defined. Thus, you can't really encode all of them with splitting.
    - pama 2y ago
      Right. I stand corrected and it makes sense. I may have misunderstood the OP. It would not make sense to encode each unicode point as a token and then start training the subword tokenizer on top unless we go to the vocabularies of many millions.
    - 2y ago
singularity2001 2y ago
probably something as byte latent tokenization, or get rid of organization altogether as kaparthy suggested

by pama 2y ago

cchance 2y ago

I still wonder why we're training models on all these languages especially when they have different alphabets etc, we've got solid translators, wouldn't it be more parameter dense to target one language for all data and tokens, and then have a layer specifically for input and output translation?

eightysixfour 2y ago
I'd be interested to know if adding more languages makes them more or less performant. It is my understanding that you have to add code for the models to perform well, for example.
- cchance 2y ago
  My issue and questions isn't so much languages but character sets, expanding say an ascii dataset to include all the non-ascii language glyphs or other unicode characters has got to balloon the parameter count
ijustlovemath 2y ago
more languages gives deeper semantic understanding; I think it only helps with diversity of data, which ultimately improves outputs
sva_ 2y ago
You'll have a lower bound on the quality of the translator you're using. There's an idea that you can generalize concepts among different languages, and that you'll benefit from the extended training corpus. As in, talking about an idea from different perspectives helps the model carve it out. But I don't have anything concrete to back that claim up.
- econ 2y ago
  Each language has unique tools. If you have a word for something or even better a whole set of words the conversation works a lot better than in a language that has nothing of the kind. English elaborately talks about all kinds of Communities. Dutch does have a word like it but it is almost never used. Or, how should we talk about the kind of snow if we have only one word? https://watchingtheswedes.com/2018/02/28/50-words-for-snow/ https://watchingtheswedes.com/2018/02/28/50-words-for-snow/
cma 2y ago
More languages helps it at novel translation tasks, models have been tested with languages not in/barely in the corpus and a translation book in context and were able to do an ok job. You'll also have things like mulimodal where you want to preserve all the tonality and emphasis in the input language.
astrange 2y ago
Bitter lesson says any kind of specialization is not worth it[0]. Also, you want to be able to have mixed language conversations, like defining a Chinese word in English. [0] but it might be worth it if you need a smaller model because then there are tradeoffs again.

amluto 2y ago

From the OP, it sounds like those tokens are generated from the UTF-8 bytes instead of from the Unicode code points. And those bytes are, taken in isolation, complete nonsense. Imagine a token that represented the right side of the letter d followed by the left side of the letter e but could also represent other mishmashes of characters. I bet the first layer of the model is mostly stuck reconstructing something resembling actual words. (UTF-8 is locally decidable. I bet that a bit of work on the token list could cause it to avoid tokens that do not align with code point boundaries.)

pama 2y ago
To be clear these tokenizers use byte-pair encoding (subword tokens) so an individual token index typically corresponds to a piece of a word; this index does not depend on any intermediate decoding of the byte stream as long as the start of the stream is a start of your input. The decoding always works left to right and always starts at the start of the stream. You could write a tokenizer that uses plain bytes and one that uses unicode code points if your tokenizer was trained on unicode and forced to keep unicode codes together (almost all are), and the results would be identical for all practical purposes.
- mmoskal 2y ago
  llama2, llama3, gpt3, and gpt4 tokenizers are all trained on UTF8 bytes and all include invalid (partial) UTF8 tokens. For llama3 it's only 256 tokens, one for each byte, but for the others it's more interesting (eg., 1361 tokens with UTF8 fragments in llama3). There is over 1M possible Unicode code points, and 150k actually defined. Thus, you can't really encode all of them with splitting.
  - pama 2y ago
    Right. I stand corrected and it makes sense. I may have misunderstood the OP. It would not make sense to encode each unicode point as a token and then start training the subword tokenizer on top unless we go to the vocabularies of many millions.
  - 2y ago

singularity2001 2y ago

probably something as byte latent tokenization, or get rid of organization altogether as kaparthy suggested