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Do you mean that all produced output must be a chain or words found in a dictionary? The real-world for humans has them creating and using non-dictionary words

Do you mean that all produced output must be a chain or words found in a dictionary? The real-world for humans has them creating and using non-dictionary words to communicate daily. A good example is "notify", defined in the dictionary. "notifier", which is not and is used to describe "a means to notify someone". The code to send an email notification is an "email notifier", then there is text message, voice call, call center call back notifiers .... All industries and organizations have jargon, custom defined words not found in a dictionary and use non distinctive acronyms. How would a ML output be useful if it cannot handle real world commutation and only lab based sanitization of in-dictionary only responses?

phh 2y ago
That's the OP's point. At the time, the community was split between word-level, which has the shortcomings you're describing, and byte-level which is uselessly compute intensive. BPE was the first reasonable in-between. BLT improves on BPE by having the the compression learnable rather than precomputed
entilzha 2y ago
(Author here) If I understand your question right, this is one of the reasons BPE is nice and the parent liked it. For any character sequence, provided the characters are in the alphabet used to create the BPE vocab, there are no unknown words/sequences. One downside of some previous tokenization methods is you could have unknown/UNK tokens, EG dictionary based methods. In our paper with bytes, we also avoid the UNK issue, since we can have an embedding for every possible byte, since it’s not that many (and for sequences of bytes we use hash embedding, although we did test n-gram lookups for the top K frequent byte n-grams in the training data).
- cs702 2y ago
  Nice work. Thank you for commenting on HN! Did you guys try using an RNN or some other kind of DNN to encode the patches?
  - entilzha 2y ago
    I don't believe so, or at least if someone tried it didn't work well enough that I remember :). Some of the motivation for the architecture changes in encoding patches stemmed from finding FLOP efficient ways to express relationships between byte sequences. E.G., having a long context window makes sense when dealing with tokens, but you don't need as long as an attention window if you're attending byte sequences to make patch representations, since the patch representations will implicitly be part of a longer context window in terms of number of patches.
    - cs702 2y ago
      Thanks for the quick reply! Interesting. I would have thought one of those "minimum viable" RNNs (like https://arxiv.org/abs/2410.01201 https://arxiv.org/abs/2410.01201) would have been ideal for this. I might tinker a bit with this :-)

phh 2y ago

That's the OP's point. At the time, the community was split between word-level, which has the shortcomings you're describing, and byte-level which is uselessly compute intensive. BPE was the first reasonable in-between. BLT improves on BPE by having the the compression learnable rather than precomputed

entilzha 2y ago

(Author here) If I understand your question right, this is one of the reasons BPE is nice and the parent liked it. For any character sequence, provided the characters are in the alphabet used to create the BPE vocab, there are no unknown words/sequences. One downside of some previous tokenization methods is you could have unknown/UNK tokens, EG dictionary based methods. In our paper with bytes, we also avoid the UNK issue, since we can have an embedding for every possible byte, since it’s not that many (and for sequences of bytes we use hash embedding, although we did test n-gram lookups for the top K frequent byte n-grams in the training data).

cs702 2y ago
Nice work. Thank you for commenting on HN! Did you guys try using an RNN or some other kind of DNN to encode the patches?
- entilzha 2y ago
  I don't believe so, or at least if someone tried it didn't work well enough that I remember :). Some of the motivation for the architecture changes in encoding patches stemmed from finding FLOP efficient ways to express relationships between byte sequences. E.G., having a long context window makes sense when dealing with tokens, but you don't need as long as an attention window if you're attending byte sequences to make patch representations, since the patch representations will implicitly be part of a longer context window in terms of number of patches.
  - cs702 2y ago
    Thanks for the quick reply! Interesting. I would have thought one of those "minimum viable" RNNs (like https://arxiv.org/abs/2410.01201 https://arxiv.org/abs/2410.01201) would have been ideal for this. I might tinker a bit with this :-)