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I disagree with what you're saying here. > The input prompts to GPT-3 and friends are not "training examples". Training examples are labelled instances of a co

I disagree with what you're saying here. > The input prompts to GPT-3 and friends are not "training examples". Training examples are labelled instances of a concept to be learned- that's PAC-Learning. LLM prompts are not examples, and they're not used in training. In the PAC learning setting, these are training examples because you use the labels to select a function, in this case the conditional output given the few shot examples. Whether or not you actually update any weights has nothing to do with "few shot learning" and never has. In the PAC setting there are no weights, just model functions that depend on the training data. EDIT: The reason you didn't hear people refer to the inputs of HMMs as "training examples" is because HMMs are very poor few-shot learners. That's why GPT-3 is interesting, because it is a good few-shot learner. You could use an HMM as a few shot learner, but computing the results is expensive and the results are not good for most tasks.

YeGoblynQueenne 4y ago
>> In the PAC learning setting, these are training examples because you use the labels to select a function, in this case the conditional output given the few shot examples. That's not right. When you give a prompt to an LLM it doesn't use it to select a function [1]. The function is already selected (its weights are trained). The prompt only invokes the function. The fact that there are no weight updates is important. Weight updates is how neural networks learn. Without weight updates neural nets can't be said to learn anything. To dredge up from the depths yet another ancient definition of machine learning, the one by Tom Mitchell, an LLM will never improve its performance no matter how many prompts you give it. Every time you leave an interaction session with GPT-3, all the information you gave it in your prompts simply ceases to exist, as far as its model is concerned. There's nothing that is remotely like "learning" in that. _______________ [1] More precisely, in PAC-Learning a learner is said to have learned a class of concepts if, given an unlabelled instance from a concept in the class it correctly labels the instance as a member of the concept, with some probability of some error and in polynomial time, or after having observed a polynomial time of examples. As you say, PAC-Learning doesn't say anything about weights, but it doesn't say anything about selecting functions, either. It's a framework within which to study learnability. That's a bit of an aside, hence the footnote.
- mgraczyk 4y ago
  It does select a function. The distribution over outputs conditioned on the input prompt is a different distribution, hence a different (random) function. It's not important that there are no weight updates. The new function, the model conditioned on a few example input-output pairs, is good at producing new outputs for unseen inputs. That is what learning is. There is a reason that everyone working on LLMs is comfortable with this definition. It really does exactly match the definition that has been used for decades.
  - YeGoblynQueenne 4y ago
    >> The distribution over outputs conditioned on the input prompt is a different distribution, hence a different (random) function. As I say in another comment (and if I understand what you mean correctly) that is not supported by observations. >> It's not important that there are no weight updates. The new function, the model conditioned on a few example input-output pairs, is good at producing new outputs for unseen inputs. To be blunt, I've seen as much evidence for that as I've seen evidence for the existence of the Sasquatch. The people who train those models don't even know what's in their datasets, let alone anyone being able to say that an "example" (i.e. a prompt) is "unseen".

YeGoblynQueenne 4y ago

>> In the PAC learning setting, these are training examples because you use the labels to select a function, in this case the conditional output given the few shot examples. That's not right. When you give a prompt to an LLM it doesn't use it to select a function [1]. The function is already selected (its weights are trained). The prompt only invokes the function. The fact that there are no weight updates is important. Weight updates is how neural networks learn. Without weight updates neural nets can't be said to learn anything. To dredge up from the depths yet another ancient definition of machine learning, the one by Tom Mitchell, an LLM will never improve its performance no matter how many prompts you give it. Every time you leave an interaction session with GPT-3, all the information you gave it in your prompts simply ceases to exist, as far as its model is concerned. There's nothing that is remotely like "learning" in that. _______________ [1] More precisely, in PAC-Learning a learner is said to have learned a class of concepts if, given an unlabelled instance from a concept in the class it correctly labels the instance as a member of the concept, with some probability of some error and in polynomial time, or after having observed a polynomial time of examples. As you say, PAC-Learning doesn't say anything about weights, but it doesn't say anything about selecting functions, either. It's a framework within which to study learnability. That's a bit of an aside, hence the footnote.

mgraczyk 4y ago
It does select a function. The distribution over outputs conditioned on the input prompt is a different distribution, hence a different (random) function. It's not important that there are no weight updates. The new function, the model conditioned on a few example input-output pairs, is good at producing new outputs for unseen inputs. That is what learning is. There is a reason that everyone working on LLMs is comfortable with this definition. It really does exactly match the definition that has been used for decades.
- YeGoblynQueenne 4y ago
  >> The distribution over outputs conditioned on the input prompt is a different distribution, hence a different (random) function. As I say in another comment (and if I understand what you mean correctly) that is not supported by observations. >> It's not important that there are no weight updates. The new function, the model conditioned on a few example input-output pairs, is good at producing new outputs for unseen inputs. To be blunt, I've seen as much evidence for that as I've seen evidence for the existence of the Sasquatch. The people who train those models don't even know what's in their datasets, let alone anyone being able to say that an "example" (i.e. a prompt) is "unseen".