I’m not sure how much lasting value this post has. My recent post here covers the same ground more carefully.
I’m not sure if this is relevant, but this post received some very critical comments, leading me to seriously question the value of continuing to write posts like this on LW. See here for a discussion about this with a reader of my blog. I did continue to write posts like this, and they have been well received, even when they reiterated my arguments here. I am curious what explains this difference, and have no good hypotheses.
If you feel like “larger language models may disappoint you” was one of the posts that reiterated your arguments here, they seem to be saying pretty different things to me? It feels like this article is fundamentally focused on talking about the GPT-3 paper whereas your later post is focused on talking about GPT-3 itself.
The later post still reiterates the main claims from this post, though.
This post: “Few-shot learning results are philosophically confusing and numerically unimpressive; the GPT-3 paper was largely a collection of few-shot learning results, therefore the paper was disappointing”
The later post: “Few-shot learning results are philosophically confusing and numerically unimpressive; therefore we don’t understand GPT-3′s capabilities well and should use more ‘ecological’ methods instead”
Many commenters on this post disagreed with the part that both posts share (“Few-shot learning results are philosophically confusing and numerically unimpressive”).
If you feel like “larger language models may disappoint you” was one of the posts that reiterated your arguments here, they seem to be saying pretty different things to me? It feels like this article is fundamentally focused on talking about the GPT-3 paper whereas your later post is focused on talking about GPT-3 itself.
The later post still reiterates the main claims from this post, though.
This post: “Few-shot learning results are philosophically confusing and numerically unimpressive; the GPT-3 paper was largely a collection of few-shot learning results, therefore the paper was disappointing”
The later post: “Few-shot learning results are philosophically confusing and numerically unimpressive; therefore we don’t understand GPT-3′s capabilities well and should use more ‘ecological’ methods instead”
Many commenters on this post disagreed with the part that both posts share (“Few-shot learning results are philosophically confusing and numerically unimpressive”).