Tokens are a big reason today's generative AI falls short
Generative AI models encounter limitations with tokenization, affecting performance in tasks like math and language processing. Researchers explore alternatives like MambaByte to address tokenization challenges, aiming to enhance model efficiency and capabilities.
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Generative AI models face limitations due to tokenization, where text is broken into smaller pieces called tokens for processing. Tokenization can introduce biases and challenges, especially in languages without spaces between words. Tokenizers treat case and digits differently, impacting model performance in tasks like math and language processing. Tokenization issues extend to languages like Chinese and Thai, affecting model efficiency and cost. Researchers are exploring alternatives like MambaByte, a byte-level model that avoids tokenization, showing promise in handling noise and improving performance. Despite challenges, new model architectures may offer solutions to tokenization limitations in generative AI. The complexity of tokenization impacts various aspects of AI model behavior, highlighting the need for advancements in this area to enhance model capabilities and efficiency.
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9 commentsTechCrunch, I respect that you have a style guide vis a vis punctuation and quotation marks, but please understand when it's appropriate to break the rules. :P
GPT4 can answer questions given to it in Base64. I would imagine it suffers some degree of degradation in ability from the extra workload this causes but I haven't seen any measurements on this.
I have wondered about other architectures to help. What happens when a little subnet encodes the (16 or 32?) characters in the neighborhood of the token into an embedding that gets attached to the top level token embedding?
Part of what makes AI interesting is that it can understand a huge number of differently phrased data. It seems like different token encodings would only be a very minor complexity compared to the variety of human language.
It doesn't really explain anything besides talking about tokenization on random levels.
You need a certain amount of data to even understand that once upon a time might be a higher level concept.
I think the future is a small word encoder model that replaces the token embedding codebook.
And here’s the reason: you can still create a codebook after training and then use the encoder model only for OOV. I’m not sure there’s an excuse not to be doing this, but open to suggestions.
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