5 ms·

> I'm literally describing how inference is done. You are very clearly not doing that. Nothing about your comment had anything to do with the internal structur

by wyager 2y ago

> I'm literally describing how inference is done. You are very clearly not doing that. Nothing about your comment had anything to do with the internal structure of LLMs. I believe you that you've set up some models with pytorch or whatever, but this seemingly hasn't translated to a sufficiently coherent mental model to make the distinction between the extrinsic optimization criterion and intrinsic behavior.

lolinder 2y ago
I'm not talking about optimization criteria or training, I'm talking about how we use the model for inference. We feed in a context and it gives a probability distribution for the next word. We sample from that distribution following some set of rules. We then update the context with the new word and repeat. That algorithm is an autocomplete algorithm. As long as that's what LLM inference looks like, all problems that we want to feed to an LLM therefore must be translated to autocomplete. I think you're making the mistake of assuming that because I use language that resembles the language used by cynics I'm therefore arguing that LLMs are useless. I'm not. All I'm saying is that we need to have an accurate mental model for the way these things work, and that mental model is autocomplete. Nearly every major failure in an LLM application was the result of failing to keep that in mind.
- wyager 2y ago
  > all problems that we want to feed to an LLM therefore must be translated to autocomplete. I don't disagree with this, but I do disagree with this earlier statement: > The further you get from autocomplete, the less reliable the resulting product Any naturally sequential problem is trivial to translate to autocomplete with minimal loss of fidelity.
  - lolinder 2y ago
    In other words, would it be fair to say that any naturally sequential problem is not very far from autocomplete? Again, I think you're putting words in my mouth and thoughts in my head that aren't there. A lot of people have reacted to AI hype by going the other way and underestimating them—that's not me. I think there are lots of problems they can solve, I just think they all boil down to autocomplete and if you can't boil it down to autocomplete you're not ready to implement it yet with an LLM.
    - danielmarkbruce 2y ago
      People fine tune LLMs for classification tasks. This is completely wrong.
      - lolinder 2y ago
        You're just going around saying people are completely wrong without reading what they wrote or providing any justification for that claim. I'm not sure how to respond because your comment is a non sequitur.
        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 ago
        That'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 ago
        If 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.

by wyager 2y ago

lolinder 2y ago

I'm not talking about optimization criteria or training, I'm talking about how we use the model for inference. We feed in a context and it gives a probability distribution for the next word. We sample from that distribution following some set of rules. We then update the context with the new word and repeat. That algorithm is an autocomplete algorithm. As long as that's what LLM inference looks like, all problems that we want to feed to an LLM therefore must be translated to autocomplete. I think you're making the mistake of assuming that because I use language that resembles the language used by cynics I'm therefore arguing that LLMs are useless. I'm not. All I'm saying is that we need to have an accurate mental model for the way these things work, and that mental model is autocomplete. Nearly every major failure in an LLM application was the result of failing to keep that in mind.

wyager 2y ago
> all problems that we want to feed to an LLM therefore must be translated to autocomplete. I don't disagree with this, but I do disagree with this earlier statement: > The further you get from autocomplete, the less reliable the resulting product Any naturally sequential problem is trivial to translate to autocomplete with minimal loss of fidelity.
- lolinder 2y ago
  In other words, would it be fair to say that any naturally sequential problem is not very far from autocomplete? Again, I think you're putting words in my mouth and thoughts in my head that aren't there. A lot of people have reacted to AI hype by going the other way and underestimating them—that's not me. I think there are lots of problems they can solve, I just think they all boil down to autocomplete and if you can't boil it down to autocomplete you're not ready to implement it yet with an LLM.
  - danielmarkbruce 2y ago
    People fine tune LLMs for classification tasks. This is completely wrong.
    - lolinder 2y ago
      You're just going around saying people are completely wrong without reading what they wrote or providing any justification for that claim. I'm not sure how to respond because your comment is a non sequitur.
      - 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 ago
        That'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 ago
        If 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.