| ▲ | bunderbunder 16 hours ago | |||||||||||||||||||||||||
GPT-3 might not have said “load bearing” as much, but a savvy interrogator could still catch it out nearly every time just asking dumb gotcha questions like, “How many Rs are there in strawberry?” | ||||||||||||||||||||||||||
| ▲ | famouswaffles 16 hours ago | parent [-] | |||||||||||||||||||||||||
Those are questions that are sidestepped with simply a different input paradigm than BPE tokenization. See the Byte Latent Transformer - https://arxiv.org/pdf/2412.09871 - where a similar scale byte latent model trained on the same dataset >>> a vanilla transformer on word and character manipulation tasks. For example, Llama 3 trained on 1T tokens scores 1.1% on a CUTE spelling benchamrk, while the equivalent byte latent equivalent trained on the same dataset scores 99.9%. Another example is 0.4% vs 48.7% on a Substitute Char benchmark. It all falls down to the same thing. Researchers are not optimizing for passing as a human. | ||||||||||||||||||||||||||
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