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Comment by a65cec93b | Hacker News Reader

> GPT-3 correctly reverses long words! But to get there, we had to teach GPT-3 the algorithm to use to get around its limitations.

Has GPT-3 really been "taught" anything here? If you don't provide an explicit example as the context of your input, GPT-3 does not retain the ability to reverse words.

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(author here) It depends a bit on how you define "retain". Most GPT-3 applications use custom "prompts" to train it for their specific use case. So in that way, the prompt is retained with every request.

You can also fine-tune GPT-3 to retain the ability to reason through problems. For example, check out this work on reasoning for grade school math: https://openai.com/blog/grade-school-math/

Isn’t this a “can submarines swim?” question?

It has performed a novel (to it) task based on instructions, and this is IMHO remarkable. It should be possible to make it retain and recall this procedure.

Everything non-sci-fi AI does is “just” an algorithm, so it won’t live up to standards of human abilities, precisely because we know how this result has been obtained.

We do not have any reason to believe that mere algorithms are incapable of living up to the standards of human abilities.

I'm saying that even when it does reach this level, we will still question whether it really knows/thinks/understands things, or is it merely a big database. Ultimately, it's a philosophical question whether a machine can be alive. Even when we build the singularity, we'll still wonder if it really fears or hates us, or is it just a dumb paperclip maximizer.

No, it isn't taught anything. GPT3 text generation is effectively a really fancy autocompletion algorithm based on the n-many previous tokens in a rolling window. You can only "teach" GPT3 something within that window, and it doesn't "learn" there, it just tries its best to generate content based on what is stored in its massive n-dimension table of graph edges for tokens.

That is also why it has such a strong propensity to lose the plot once you are outside of that window size and it's generating new content based on self-generated content.

You can update the "graph edges" with content longer than the window by fine tuning: https://beta.openai.com/docs/guides/fine-tuning

Yes, training the model is where it learns, not in prompts. Prompting might be considered meta-learning but it will always need a reference point given to it from its training data, and beyond the prompt the original model is never altered.

Eh, one could argue that this is similar to the short term/long term memory divide in humans. We tend to suck at new things until we sleep on it and update our weights...

I got it close:

Build a character array in Python for the string "hellohackernews":

['h', 'e', 'l', 'l', 'o', 'h', 'a', 'c', 'k', 'e', 'r', 'n', 'e', 'w', 's']

Reverse the order of the python array characters:

['s', 'w', 'e', 'n', 'r', 'a', 'k', 'c', 'a', 'h', 'o', 'l', 'l', 'e', 'h']

Er, maybe not...

Reassemble the second array into a string without spaces:

"swenrakcaholles"

I'm not sure how you define teaching, but for me getting shown an example and then repeating it successfully with another input does mean teaching/learning. I know the model doesn't update though, let's not focus on that now.

If anthropomorphizing bothers you, then we could just use "prompting", but I feel teaching is a good enough approximation here.

It's repeating based on what the trained model has given it about situations where instructions possibly similar to the instructions given are specified and which were about reversing strings in general.

If the author messed with temperature and retried their failing prompt enough times, or simply reworded it a little differently, they might also get the correct answer.

Isn't that true with humans too? Sometimes rephrasing the question or simply telling them to try again will lead them to the right answer

You're right for GPT 3, but it's an example of chain of thought reasoning, which seems to be a new area of research [1] and might get integrated into newer versions:

[1] https://arxiv.org/abs/2201.11903

That's easy to solve. Prepare all K-12 text books as prompts, and train another GPT-N to go from input to those prompts, then feed these prompts to the current GPT-3.

Can we get a GPT-N-3 this way to do SAT?

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