> How does it respond to similar questions?

Well, one of the more interesting examples in the article is where Garry Smith took a question that had received only a vague equivocating answer, and repeated it the next day, this time getting a straightforward and correct answer. When he followed up with a very similar question on the same topic, however, GPT-3 reverted to replying with the same sort of vague boilerplate it had served up the day before. One would have to be quite determined to not find out, I think, if one was not curious about how that came about.

Guess: Some human saw the q & a. They realized it wasn't good. They uploaded some example of a phrase and the meaning, which would fix it. They were kinda lazy and just fixed that specific scenario.

Doesn't that still call into question the quality of GPT-3? Surely such a large model should be able to extrapolate to "which is better: a or b?" from "is a better than b?" when only provided with the latter.

There is no definitive answer to this question, as it depends on the situation.

It doesn’t call it into question for me, but perhaps I just had lower expectations to start with.

I forget where I saw this comparison so I can’t link to it, but the last few years in AI are like waking up and finding dogs can talk: while some complain they’re not the worlds greatest orators, I find it amazing they can string a few genuinely coherent sentences together and maintain a contextual thread over multiple responses even half the time.

Perhaps you are thinking of Scott Aaronson's "AlphaCode as a dog speaking mediocre English"? [1]

I agree with the sentiment, but to continue your analogy, if OpenAI is using people to improve the answers to specific questions, it is a bit like learning that Cicero, Lincoln and Churchill were merely reading the work of speechwriters.

There is an argument that it does not matter how GPT-3 gets to its answers - after all, for a long time, the main approach to AI was for people to write a lot of bespoke rules in an attempt to endow a computer with common sense and knowledge, so GPT-3 + instructGPT might be described as a hybrid of machine learning and the old approach.

If OpenAI wishes to pursue that path, it is fine by me (as if my opinion matters!) but, because the perception of GPT-3 depends very strongly on how its output looks to human readers, it is obviously misleading if some of the most impressive replies were largely the result of specific human intervention. The issue is transparency: I would just like to know, when I read a reply, if this was the case, and it would not help OpenAI for it to ignore the call, in this article, for it to be clear about this.

There is another argument that says that, given how GPT-3 works, it is unreasonable to expect it to give good answers in these cases - but that's the point! It looks really impressive when GPT-3 apparently does so, but not if they were effectively hard-coded.

[1] https://scottaaronson.blog/?p=6288

Thanks for the link, I was either thinking of that or someone who was referencing that.

Perhaps, I'm not sure. I guess it depends on what's causing it to stumble.

I'd just as easily believe that someone updated the model, it overfit to the input, and so minor changes gave incorrect answers.

It is, of course, possible that, just by chance, someone happened to update the model in exactly the way that would change the model's response, to one specific question, from a generic evasion to a specific correct answer, yet overfitting so that very similar questions still get the generic evasion. It is even possible that, by chance, this happened within a day after Garry submitting the particular phrasing of the question for which the updated model does give a correct answer. That it is possible is not enough to satisfy my curiosity, however - but then, I don't rank it as being at least as likely as any other scenario.

On the other hand, if your scenario involves someone updating the model (or, more likely, InstructGPT) in response to Garry's question, with the intent of having GPT3 return the correct answer to that question, I am not seeing how that would be materially different, in any relevant sense, from hard-coding the answer.

I'll just drop the fact here, that 15% of all google searches in 2017 were new and unique.

https://blog.google/products/search/our-latest-quality-impro...