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> Remarkably, conditioning the model on such an “example-based specification” effectively enables the model to adapt on-the-fly to novel tasks whose distributio

> Remarkably, conditioning the model on such an “example-based specification” effectively enables the model to adapt on-the-fly to novel tasks whose distributions of inputs vary significantly from the training distribution. The idea that simply minimizing the negative log loss of a single next-word-prediction objective implies this apparent optimization of many more arbitrary tasks – amounting to a paradigm of “learning” during inference time – is a powerful one, and one that raises many questions. http://ai.stanford.edu/blog/in-context-learning/ http://ai.stanford.edu/blog/in-context-learning/

YeGoblynQueenne 4y ago
Hey man. Give some context to links please. Don't just dump them like that... But thanks for the link, it seems like an interesting read though I'm too tired now to read it (but not for spouting off on HN... i know right). >> Remarkably, conditioning the model on such an “example-based specification” effectively enables the model to adapt on-the-fly to novel tasks whose distributions of inputs vary significantly from the training distribution. That would be indeed interesting if it was the case, but as we have seen in practice, it isn't. For example, GPT-3 is great in addition and subtraction with two- and three-digit numbers, but its performance deteriorates rapidly after that, plus it's bad at multiplication and completely incapable of division [1]. That's exactly what you'd expect of a model trained on a dataset where "easy" arithmetic problems predominate, i.e. the internet, and not at all what you'd expect of a model that can somehow magickally generalise beyond its training distribution without even being trained, and without even retaining any memory of its "examples" (i.e. prompts). In any case, the way to convince me that I'm wrong is to do the good, old-fashioned hard work of proving LLMs' learning ability, like people used to do when it was all hairy stuff like statistical learning theory and VC-dimension and so on. But that's too much hard work and people prefer to poke LLMs with arbitrary prompts instead nowadays. And I agree that it's more fun that way, but at some point you have to give up the fun and try to understand exactly what's going on with a complex system. ____________________ [1] See Figure 3.10 in "Language models are few-shot learners". Strange, I could have sworn there used to be a "large" in the title.
- YeGoblynQueenne 4y ago
  Edit: @visarga, I was kind of expecting you to tell me about the commas in arithmetic. I wonder whether you didn't because it's obvious to yo, too, that that is evidence refuting the claim of learning out-of-distribution concepts. As in, yes, GPT-3 gets better at arithmetic with comma-separated values, but that's because the commas help it overfit even more.

YeGoblynQueenne 4y ago

Hey man. Give some context to links please. Don't just dump them like that... But thanks for the link, it seems like an interesting read though I'm too tired now to read it (but not for spouting off on HN... i know right). >> Remarkably, conditioning the model on such an “example-based specification” effectively enables the model to adapt on-the-fly to novel tasks whose distributions of inputs vary significantly from the training distribution. That would be indeed interesting if it was the case, but as we have seen in practice, it isn't. For example, GPT-3 is great in addition and subtraction with two- and three-digit numbers, but its performance deteriorates rapidly after that, plus it's bad at multiplication and completely incapable of division [1]. That's exactly what you'd expect of a model trained on a dataset where "easy" arithmetic problems predominate, i.e. the internet, and not at all what you'd expect of a model that can somehow magickally generalise beyond its training distribution without even being trained, and without even retaining any memory of its "examples" (i.e. prompts). In any case, the way to convince me that I'm wrong is to do the good, old-fashioned hard work of proving LLMs' learning ability, like people used to do when it was all hairy stuff like statistical learning theory and VC-dimension and so on. But that's too much hard work and people prefer to poke LLMs with arbitrary prompts instead nowadays. And I agree that it's more fun that way, but at some point you have to give up the fun and try to understand exactly what's going on with a complex system. ____________________ [1] See Figure 3.10 in "Language models are few-shot learners". Strange, I could have sworn there used to be a "large" in the title.

YeGoblynQueenne 4y ago
Edit: @visarga, I was kind of expecting you to tell me about the commas in arithmetic. I wonder whether you didn't because it's obvious to yo, too, that that is evidence refuting the claim of learning out-of-distribution concepts. As in, yes, GPT-3 gets better at arithmetic with comma-separated values, but that's because the commas help it overfit even more.