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> As a result, the model isn’t trained on understanding the useRalativeImagePath token, and so it outputs something that isn’t a valid token. That isn't how LL
by minimaxir 3y ago
> As a result, the model isn’t trained on understanding the useRalativeImagePath token, and so it outputs something that isn’t a valid token.
That isn't how LLMs generate tokens. Each step outputs a logit for each possible token in the tokenizer (100k in the case of GPT-3.5), then softmaxes the logits to covert them into probabilities, and samples from them depending on temperature to get the token to be used.
It's possible something in the tokenizer BPE merge process breaks due to the rare token, which can be verified offline using tiktoken. But if GPT-4 works, and since GPT-3.5 and GPT-4 use the same tokenizer, then that's likely not the issue.
- npsomaratna 3y agoCorrect me if I'm wrong—but we don't know if GPT-4 uses the same tokenizer as GPT-3.5, right?
- minimaxir 3y agoOpenAI's web tokenizer demo confirms it: https://platform.openai.com/tokenizer https://platform.openai.com/tokenizer
- shawntan 3y agoExactly this. The tokens generated should always be valid, unless some post-processing layer between the model's output and the user interface detects for some keywords which it would prefer to filter out. In which case I suppose there is another commonly seen error message that appears?
- SirSegWit 3y agoNot really, right? There are a ton of special tokens, like start of sequence etc., so what happens if there are two start of sequences predicted? It's a valid token but cannot really be turned into something sensible, so it throws an error when converting tokens to plain text?
- minimaxir 3y agoSpecial tokens are handled by the application, not the model. They are still output before then.
- londons_explore 3y agoI suspect more likely this token is simply blacklisted after the r/counting incident - ie. any response containing it will now return an error.