People do this a lot with BERT, and it has its own problems -- the first section of this recent paper gives a good overview.

Then of course there is plenty of work trying to mitigate those problems, like that paper . . . but there are still various ways of doing so, with no clear consensus. So a more general statement of few-shot's promise might be "you don't have to worry about which fine-tuning setup you're going to use, out of the many available alternatives, all of which have pitfalls."

To be fair, it's not an apples-to-apples comparison.

GPT-3 few-shot learning gets to use less data. (Although much of superGLUE has tiny train sets, so this gap isn't as big as it sounds.) And with GPT-3 you don't have the storage overhead of a separate trained model for every task.

Back when I wrote this post, I really did not realize that OpenAI was serious about few-shot learning as a practical, competitive approach. I had assumed it was meant as a conceptual demonstration of meta-learning, or a new way to probe what LMs "know."

In other words, I implicitly assumed "oh, of course they aren't planning [something like the OpenAI API], it'd be uncharitable to assume they actually think this is a practical approach." Now it's clear that they do think that, which makes for a different conversation than the one I had expected here. (I'm still bearish on the approach, though.)

They do discuss this a little bit in that scaling paper, in Appendix D.6. (edit: actually Appendix D.5)

At least in their experimental setup, they find that the first 8 tokens are predicted better by a model with only 8 tokens its its window than one with 1024 tokens, if the two have equally many parameters. And that later tokens are harder to predict, and hence require more parameters if you want to reach some given loss threshold.

I'll have to think more about this and what it might mean for their other scaling laws... at the very least, it's an effect which their analysis treats as approximately zero, and math/physics models with such approximations often break down in a subset of cases.

On the reading of the graphs:

All I can say is "I read them differently and I don't think further discussion of the 'right' way to read them would be productive."

Something that might make my perspective clear:

• when I first read this comment, I thought "whoa, that 'phase change' point seems fair and important, maybe I just wasn't looking for that in the graphs"
• and then I went back and looked at the graphs and thought "oh, no, that's obviously not distinguishable from noise; that's the kind of non-monotonic bouncing around that I expect when you need more data per plotted point to get a reasonable estimate; that Squad V2 graph looks like the other 5 reading comp graphs except with more noise," etc. etc.

I don't expect this will convince you I'm right, but the distance here seems more about generic "how to interpret plots in papers" stuff than anything interesting about GPT-3.

On this:

I can't think of a coherent model where both of these claims are simultaneously true; if you have one, I'd certainly be interested in hearing what it is.

Roughly, my position is that transformer LMs are very impressive and know all sorts of things, even at small scale, although they know them "less noisily" as the scale grows.

The intended connotation of my stance that "fine-tuning will outperform few-shot" is not "haha, transformers are limited, they will never stand on their own without supervised training, boo transformers!" If anything, it's the opposite:

• I think transformers have some limits (e.g. physical / spatial stuff). But, already at the 1.5B scale if not before, they display a very-real-if-noisy understanding of the linguistic phenomena probed by most NLP benchmarks.
• I think fine-tuning has shown itself to be a remarkably effective way to "get at" this knowledge for downstream tasks -- even with small data sets, not far in scale from the "data sets" used in few-shot.
• So, I don't understand what few-shot gets us in terms of ways to probe transformer understanding (we already had a great one) or as a demo of language understanding (what I see in my own generation experiments, at two orders of magnitude lower, impresses me far more than the few-shot results).

Again, I engage with this stuff foremost as someone who is very impressed transformer LMs as text generators and has interacted with them a lot in that modality.

So, this all feels a bit like being a dog owner who reads a new paper "demonstrating dogs' capacity for empathy with humans," is unimpressed w/ it's methodology, and finds themselves arguing over what concrete model of "dog empathy" they hold and what it predicts for the currently popular "dog empathy" proxy metrics, with a background assumption that they're some sort of dog-empathy-skeptic.

When in fact -- they believe that of course their dog empathizes with them, and they find the methodology of the paper awkwardly under-equipped to explore this complex, and very clearly real, phenomenon.

I've already seen GPT-2 display vast declarative knowledge and use words in subtle context-dependent ways, and pick up the many-faceted nuances implied in a prompt, and all those things. When I see it again, but with ~100x parameters, and in a contrived experimental setting where ~1.5B models technically fare poorly even if I've seen them do that kind of thing in real life . . . should I be impressed?

I agree with you about hype management in general, I think. The following does seem like a point of concrete disagreement:

It sounds like you expected "GPT" to mean something more like "paradigm-breaker" and so you were disappointed, but this feels like a ding on your expectations more than a ding on the paper.

If the paper had not done few-shot learning, and had just reviewed LM task performance / generation quality / zero-shot (note that zero-shot scales up well too!), I would agree with you.

However, as I read the paper, it touts few-shot as this new, exciting capability that only properly emerges at the new scale. I expected that, if any given person found the paper impressive, it would be for this purported newness and not only "LM scaling continues," and this does seem to be the case (e.g. gwern, dxu). So there is a real, object-level dispute over the extent to which this is a qualitative jump.

I'm not sure I have concrete social epistemology goals except "fewer false beliefs" -- that is, I am concerned with group beliefs, but only because they point to which truths will be most impactful to voice. I predicted people would be overly impressed with few-shot, and I wanted to counter that. Arguably I should have concentrated less on "does this deserve the title GPT-3?" and more heavily on few-shot, as I've done more recently.

Are there bits of evidence against general reasoning ability in GPT-3? Any answers it gives that it would obviously not give if it had a shred of general reasoning ability?

In the post I gestured towards the first test I would want to do here -- compare its performance on arithmetic to its performance on various "fake arithmetics." If #2 is the mechanism for its arithmetic performance, then I'd expect fake arithmetic performance which

• is roughy comparable to real arithmetic performance (perhaps a bit worse but not vastly so)
• is at least far above random guessing
• more closely correlates with the compressibility / complexity of the formal system than with its closeness to real arithmetic

BTW, I want to reiterate that #2 is about non-linguistic general reasoning, the ability to few-shot learn generic formal systems with no relation to English. So the analogies and novel words results seem irrelevant here, although word scramble results may be relevant, as dmtea says.

----

There's something else I keep wanting to say, because it's had a large influence on my beliefs, but is hard to phrase in an objective-sounding way . . . I've had a lot of experience with GPT-2:

• I was playing around with fine-tuning soon after 117M was released, and jumped to each of the three larger versions shortly after its release. I have done fine-tuning with at least 11 different text corpora I prepared myself.
• All this energy for GPT-2 hobby work eventually convergent into my tumblr bot, which uses a fine-tuned 1.5B with domain-specific encoding choices and a custom sampling strategy ("middle-p"), and generates 10-20 candidate samples per post which are then scored by a separate BERT model optimizing for user engagement and a sentiment model to constrain tone. It's made over 5000 posts so far and continues to make 15+ / day.

So, I think have a certain intimate familiarity with GPT-2 -- what it "feels like" across the 4 released sizes and across numerous fine-tuning / sampling / etc strategies on many corpora -- that can't be acquired just by reading papers. And I think this makes me less impressed with arithmetic and other synthetic results than some people.

I regularly see my own GPT-2s do all sorts of cool tricks somewhat similar to these (in fact the biggest surprise here is how far you have to scale to get few-shot arithmetic!), and yet there are also difficult-to-summarize patterns of failure and ignorance which are remarkably resistant to scaling across the 117M-to-1.5B range. (Indeed, the qualitative difference across that range is far smaller than I had expected when only 117M was out.) GPT-2 feels like a very familiar "character" to me by now, and I saw that "character" persist across the staged release without qualitative jumps. I still wait for evidence that convinces me 175B is a new "character" and not my old, goofy friend with another lovely makeover.

what, in my view, are the primary implications of the GPT-3 paper--namely, what it says about the viability of few-shot prediction as model capacity continues to increase

This seems like one crux of our disagreement. If I thought the paper shows a clear trend, with room to grow, toward much greater performance few-shot learning with even bigger models, I would be more impressed with "few-shot + large LM" as an approach.

I don't think it shows that. The clearest evidence on this subject, IMO, is the many plots in their Appendix H. On a large fraction of the individual downstream tasks, few-shot learning has either

• a scaling trend with a clearly defined shape that is mostly flat by the 175B point, with a remaining gap vs. fine-tuning that seems unlike to be closed (examples: WiC, MultiRC, ReCoRD, PhysicaQA, OpenBookQA, at least 5 of the 6 reading comprehension tasks, ANLI)
• a very noisy trend where, due to noise, returns to scale might be large but might just as well be near zero (examples: BoolQ, CB, WSC)

The scaling trend is more encouraging on certain downstream tasks (COPA, ARC, Winogrande, many the MT tasks), on "less downstream" tasks that essentially probe language modeling skill in a different way (cloze/completion), and on synthetic tasks.

On average, there is a trend toward slow but steady growth with scale (Fig 1.3), but this masks the great across-task variance catalogued above. The scaling picture for few-shot is very different from the scaling picture for LM loss itself, which as catalogued in another OpenAI paper is remarkably smooth and predictable, and which (as GPT-3 shows) continues smoothly to 175B.

I find it difficult to express just what I find unimpressive here without further knowledge of your position. (There is an asymmetry: "there is value in this paper" is a there-exists-an-x claim, while "there is no value in this paper" is a for-all-x claim. I'm not arguing for-all-x, only that I have not seen any x yet.)

All I can do is enumerate and strike out all the "x"s I can think of. Does few-shot learning look promising in the scaling limit?

• As a tool for humans: no, I expect fine-tuning will always be preferred.
• As a demonstration that transformers are very generic reasoners: no, we still see a wide spread of task performance despite smooth gains in LM loss, with some of the most distinctive deficits persisting at all scales (common sense physics, cf section 5), and some very basic capabilities only emerging at very large scale and noisily even there (arithmetic).
• As an AGI component: no. Because few-shot learning on most tasks shows no clear scaling trend toward human level, any role of transformers in AGI will require more effective ways of querying them (such as fine-tuning controlled by another module), or non-transformer models.

Since I'm not feigning ignorance -- I was genuinely curious to hear your view of the paper -- there's little I can do to productively continue this conversation.

Responding mainly to register (in case there's any doubt) that I don't agree with your account of my beliefs and motivations, and also to register my surprise at the confidence with which you assert things I know to be false.

Perhaps I wasn't clear -- when I cited my experience as an ML practitioner, I did so in support of a claim about whether the stated capabilities of GPT-3 sound useful, not as a point about what those capabilities are.

I don't think the practical value of very new techniques is impossible to estimate. For example, the value of BERT was very clear in the paper that introduced it: it was obvious that this was a strictly better way to do supervised NLP, and it was quickly and widely adopted.

(I suppose it's conceivable that few-shot learning with a large model is "secretly useful" in some way not conveyed in the paper, but that's true of any paper, so if this proves anything then it proves too much.)

A smell test: what do you think your past experience would have predicted about the performance of a 175B-parameter model in advance?

Above I argued this question was orthogonal to my point, but to answer it anyway: I'd certainly predict better performance on LM tasks, as a simple extrapolation of the existing "biggening" research (GPT-2 at 1.5B parameters, Megatron-LM at 8.3B, T5 at 11B, T-NLG at 17B).

For downstream tasks, I'd expect similar scaling: certainly with fine-tuning (given T5's success on SuperGLUE) though GPT-3 was not fine-tuned, and also with unsupervised approaches (zero-shot, few-shot) given the reported scaling of GPT-2 zero-shot with model size (GPT-2 Fig 1).

I also would have predicted that fine-tuning still out-performs unsupervised approaches by a large margin on most tasks, a gap we observe with unsupervised GPT-3 vs. fine-tuned smaller models (presumably comparing to fine-tuned 175B models would yield an even larger gap).

I alluded to all this in the post, as did the GPT-3 authors in their paper: the results demonstrate that existing trends continue up to 175B. As Daniel Kokotajlo says, the new observation confirms an already familiar, though previously untested, prediction.

It sounds like you think I'm nitpicking relatively minor points while ignoring the main significance of the paper. What do you think that main significance is?

I can see an argument that the value of few-shot LM prediction is its potential flexibility as a generic tool -- it can presumably do many tasks that are not standard benchmarks, weren't in the paper, etc.

Given my past ML experience, this just doesn't sound that promising to me, which may be our disconnect. In practical work I tend to find that a few days' work preparing a supervised dataset on my exact problem domain beats anything I can produce without that dataset. Few-shot learning apparently trades that few days of work for another non-zero time investment (finding the right prompt and output-reading methodology), generally worse performance, and (pending distillation successes) vastly larger compute requirements.