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The justification is that people are fine tuning LLMs for classification. They take out the last layer, replace it with a layer which maps to n classes instead
by danielmarkbruce 2y ago
The justification is that people are fine tuning LLMs for classification. They take out the last layer, replace it with a layer which maps to n classes instead of vocab_size, and the training data Y's aren't next word, they are a class label (I have a job which does binary classification, for example)
It's just completely wrong to say everything in LLM land is autocomplete. It's trivial and common to do the above.
- lolinder 2y agoThat's still autocomplete. You use it by feeding in a context (all the text so far) and asking it to produce the next word (your fine-tuned classification token). The only difference is you don't ask for more tokens once you have one. That's a very clever way of reducing a problem to autocomplete, but it doesn't change the paradigm.
- danielmarkbruce 2y agoIf an email says "Respond to win $100 now!" and a classifier has it as 99%/1% for two classes representing spam/not spam, "spam" is not a sensible next token, it's a classification. The model is not trying to predict the next token, it's trying to classify the entire body of text. The training data isn't a bunch of samples where y is whatever came after those tokens. It's a silly way to think about it. Have you seen how people are fine tuning for classification? It's not like fine tuning for instruction or summarization etc, which are still using next token prediction and where the last layer is still mapping to vocab_size outputs.