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I don't think this is an accurate representation of LLM understanding. The significance of answering questions given in Base64 is that it can give an answer wi
by Lerc 2y ago
I don't think this is an accurate representation of LLM understanding. The significance of answering questions given in Base64 is that it can give an answer without writing out the human readable form first. That means it is understanding language in a non-language tokenised form.
This does not extend to being able to manipulate text and words well, such as string reversal or word scrambling, but it handles input in a wide range of permutations that are not conducive to tokenisation
Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text https://arxiv.org/abs/2311.18805 https://arxiv.org/abs/2311.18805
- yorwba 2y agoMy point was that it doesn't matter so much what the tokens look like as long as the model has seen them often enough during training. English-in-base64 superficially appears completely different from English, but they're actually in 1-to-1 correspondence and follow the same rules. The same holds for misspelled or badly-OCR'd text; the tokenizer actually has tokens for them and the model has seen enough of them to handle heavily-distorted text. But represent the same text using different but theoretically valid tokens that would not normally be produced by the tokenizer and all bets are off.