Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, one can choose a metric which leads to the inference of an emergent ability or another metric which does not. Thus, our alternative suggests that existing claims of emergent abilities are creations of the researcher’s analyses, not fundamental changes in model behavior on specific tasks with scale. We present our explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities, (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show how similar metric decisions suggest apparent emergent abilities on vision tasks in diverse deep network architectures (convolutional, autoencoder, transformers). In all three analyses, we find strong supporting evidence that emergent abilities may not be a fundamental property of scaling AI models.
This result seems important for two reasons:
If AI abilities are predictable, then we can forecast when we’ll get dangerous capabilities ahead of time, rather than being taken by surprise. This result strengthens the case for a research program of devising a ton of interesting benchmarks to measure how capabilities are improving as a function of scale.
It provides some evidence against the idea that “understanding is discontinuous”, and that important AI abilities will suddenly click together at some level, which is a very loose description of what I understood to be one of the primary intuitions behind AI foom.
I think there is something valuable in this kind of work, but also, my reaction to this continues to be pretty similar to Gwern’s and Eliezer’s reaction to similar discussions of forecasting AI progress:
I don’t know what to do with some kind of abstract graphs that continue in a straight line, if I don’t know how performance on that abstract graph is related to actual concrete tasks whose performance I care a lot about.
I don’t know at what level of perplexity you can refactor codebases autonomously. I don’t know at what level of perplexity you can do novel biology research and develop novel pathogens. I don’t know at what level of perplexity I get a system that can meaningfully recursively improve itself and its training process.
It is still interesting that there might exist metrics on which progress over time is stable, though in the absence of finding how those metrics relate to the real world outcomes I care about, I don’t really know what to do with that.
I wrote a shortform on this:
ETA I think that this is seriously dependent on the training data modalities; GPT4 does not have spatial awareness. I think the informativeness of comvergently ordered developmental milestones is seriously reduced because we seem to be in the “spam LLM progress” world, and not the “train multiagent RL setups in simulated 3D environments” world.
Deepmind was very much on that latter path.
Agreed, but that path is far less successful right now.
What can I read to learn more about why that path was less successful?
If you have some task like “ability to do hacking” and you think it’s well measured by some benchmark (which seems like something we could plausibly design), then this result seems to indicate that performance on this task will scale predictably with scale, as long as you know how to do the right measurement to adjust for non-linear scaling.
In other words, as long as you know how performance will increase with scale, you could fairly precisely predict what scale is necessary to obtain some arbitrary level of performance on a well-measured metric, before you’ve actually reached that level of scale. That seems like a useful thing to know for many of the same reasons found in your comment.
Yes, but I think that’s exactly what I haven’t seen. When I’ve seen benchmarks that try to do this, I’ve seen either:
That specific benchmark is not actually very smooth OR
The relationship of that benchmark to the task at hand came apart at unexpected time
Though to be clear, I also haven’t really seen anyone try this very hard (and the data I’ve seen has come more from trying to forecast things like videogames and go-performance, which haven’t seen much data in recent years where things are maybe more stable).
As far as I can tell this paper doesn’t really talk about this though. Though maybe I’ve missed something. I’ve only skimmed it.
Can you give some examples?
I don’t think people have created good benchmarks for things like “ability to hack into computers” but I suspect this is partly because relatively little effort has gone into making good benchmarks IMO. Even for relatively basic things like mathematical problem solving, we have very few high quality benchmarks, and this doesn’t seem explained by people trying hard but failing. I suspect we just don’t have that much effort going into creating good benchmarks.
But we do have lots of benchmarks for non-useful things, and the paper is just saying that these benchmarks show smooth performance.
Insofar as you’re saying that progress on existing benchmarks doesn’t actually look smooth, it sounds like you’re not responding to the contribution of the paper, which was that you can perform a simple modification to the performance metric to make performance look smooth as a function of scale (e.g. rather than looking at accuracy you can look at edit distance). Perhaps you disagree, but I think the results in this paper straightforwardly undermine the idea that progress has been non-smooth as measured by benchmarks.
I’d particularly like to see a specific example of “relationship of that benchmark to the task at hand came apart at unexpected time”.
Sorry for not responding to this. Examples do seem great, though digging up the exact charts I remember has turned out to be a bit of a longer time investment.
Some quick things I remembered feeling not that informative:
Go performance measured in ELO felt pretty hard to forecast from this kind of graph
Things like “When does chain-of-thought reasoning work?” for LLMs
LLM performance on various arithmetic tasks
Things like Alphafold, where I feel like there was basically no precursor. I remember there being forecasts about DL and protein folding, and I feel like none of them were very informative about when it would actually fall.
Sorry again for not linking to things. I might get around writing a post on this, since I do think it really deserves more exploration, but time is short these days.
I think asking for non-smoothness to call something an emergent property is unreasonable. If a performance graph is precisely an S-curve along a reasonable metric, it is reasonable to call that emergent, although it is perfectly smooth you can reparametrize to make it seem linear etc.
I haven’t looked at the paper to see what it’s substance is, but from the description alone it could be a mathematical sleight of hand.
Couldn’t the opposite critique easily be made? If some metric looks linear, then you could easily reparameterize it to make it look non-linear, and then call it emergent. That makes any claim about emergence trivial, if all you mean by emergence is that it arises non-linearly.
The central claim about emergent abilities, as I understood it, was that such abilities cannot be predicted ahead of time. But the fact that you can reparameterize any metric to make it linear, and then predict when it will reach some threshold seems like an extremely important fact, if true.
Compare two possible claims about some emergent ability:
“At the 10^28 training FLOP level, LLMs will suddenly get the ability to hack into computers competently.”
“At some training FLOP level—which cannot be predicted ahead of time—LLMs will suddenly get the ability to hack into computers competently.”
Both claims are worrisome, since both imply that at some point we will go from having LLMs that can’t hack into other computers, to LLMs that can. But I would be way more worried if the second claim is true, compared to the first.
Of course you can pick a reparameterization in hindsight, but without the benefit of hindsight, which reparameterization, exactly...?
What is interesting about emergence is that it happens on ‘natural’ parameterizations of metrics, the ones people come up with in advance of knowing the results from scaling, as opposed to retrodicting/curve-fitting ad hoc measures to make an emergence go away. No one designed any of these Big-Bench or other tasks to display emergence, and most of the initial dozen or so examples weren’t even particularly highlighted by the original authors back when I was collecting them to try to convince people that this was an actual thing which actually happened and was worth trying to understand (particularly connections to inner-monologue, hidden scaling, and U-shaped scaling).
When emergence happens on an obvious natural metric like accuracy, chosen independently of any scaling considerations at all, which often maps onto real world rewards and loss functions, then I am surprised. When un-emergence is retrodicted by the choice of metrics like… [checks notes]… ‘arithmetic accuracy expressed as a function of edit distance on BPE tokens’ (and a different one for each un-emergence) in order to explain away previously observed emergence and this retrodiction is being advertised to all and sundry as evidence of ‘predicting emergence’, then I am surprised in an entirely different way.
It’s not clear to me that edit distance or brier score are much less natural metrics than accuracy or multiple choice grade. I agree that we should have a presumption here since accuracy and multiple choice grade were chosen first, but the presumption seems pretty weak to me.
I find it easy to imagine wanting to give a model partial credit for giving answers that are close to correct even before knowing anything about emergence. One plausible theory is that awarding partial credit might not have been salient to researchers because it’s not normally how we evaluate human students. But, our choice for how we evaluate human students seems more a function of evaluation costs and lack of access to output probabilities than anything deep about measuring performance.
For these reasons, I don’t really find the metrics used in the papers ad hoc, except to the extent that “award partial credit for answers that are close to correct” is ad hoc. One prediction I’d probably make is that if we continue to use the same measures (token edit distance and brier score) then we’ll continue to see non-discontinuous progress on most benchmarks, by these measures. If true, that would at least partially falsify the claim that we were merely doing post-hoc curve fitting.
ETA: the paper says that in >92% of cases, emergence is only observed on two metrics: (1) “Multiple Choice Grade”, and (2) “Exact String Match”. I agree that Multiple Choice Grade is a fairly “natural” metric, but “Exact String Match” is less natural, and it doesn’t seem very interesting to me that we see emergence under that choice.
You can reparametrize any monotonous function to make it linear.
This can be used to predict the function
Are wildly different claims. The point is that it’s always easy to do 1. in retrospect and this has no bearing whatsoever on 2.
I think we would agree that (Log-) Flops or parameters or some mild combination of those would count as a reasonable metric?
I’m not a statistician, but from what I know it should be extremely hard to predict S-curves before their inflection point, in particular if there’s no guarantee that what you’re predicting is literally a logistic function.
That being said, trying to create benchmarks for all kinds of tasks seems like a reasonable thing to do in an case.
I’m so torn on this paper -I think it makes a reasonable point that many claims of emergence are overrated and that it’s easy to massage metrics into a single narrative. But also, I think the title and abstract are overclaiming clickbait—obviously models have emergent abilities!! Chain of thought and few shot learning are just not a thing smaller models can do. Accuracy is sometimes the right metric, etc. It’s often overhyped, but this paper way overclaims
In the Quanta Theory of Neural Scaling, individual token tasks (quanta) occupy some continuum between monogenic (non-linear/emergent) and polygenic (smooth linear). Seems reasonable that some tasks have circuit solution dependencies that work out to being more multiplicative/combinatoric than additive—ie circuit Z requires both X and Y, rather than X or Y.
Strong upvote because I want to signal boost this paper, though I think “It provides some evidence against the idea that “understanding is discontinuous”″ is too strong and this is actually very weak evidence.
Main ideas:
Emergent abilities, defined as being sharp and unpredictable, sometimes go away when we adopt different measurement techniques, or at least they become meaningfully less sharp and unpredictable.
Changing from non-linear/discontinuous metrics (e.g., Accuracy, Multiple Choice Grade) to linear/continuous metrics (e.g., Token Edit Distance, Brier Score) can cause lots of emergent abilities to disappear; Figure 3, much of the paper.
The authors find support for this hypothesis via:
Using different metrics for GPT math performance and observing the results, finding that performance can look much less sharp/unpredictable with different metrics
Meta-analysis: Understanding alleged emergent abilities in BIG-Bench, finding that there is not very much of it and 92% of emergent abilities appear when the metric is Multiple Choice Grade or Exact String Match; these are metrics we would expect to behave discontinuously; Figure 5. Additionally, taking the BIG-Bench tasks LaMDA displays emergence on and switching from Multiple Choice Grade to Brier Score causes emergence to disappear
Inducing emergence: Taking models and tasks which do not typically exhibit emergence and modifying the metric to elicit emergence. Figures 7, 8.
Sometimes emergent abilities go away when you use a larger test set (the small models were bad enough that their performance was rounding to zero on small test sets); Figure 4 compared to Figure 3 top. This may work even if you are still using a non-linear metric like Accuracy.
Observed emergent abilities may be in part due to sparsely sampling from models with lots of parameters (because it’s costly to train multiple); Figure 7.
What I’m taking away besides the above:
I think this paper should give hope to those trying to detect deception and other dangerous model capabilities. While the downstream tasks we care about might be quite discontinuous in nature (we might be fine with an AI that can design up to 90% of a pathogen, but very dead at 100%), there is hope in identifying continuous metrics that we can measure which are correlated. It’s likely pretty hard to design such metrics, but we would be shooting ourselves in the foot to just go “oh deception will be emergent so there’s no way to predict it ahead of time.” This paper gives a couple ideas of approaches we might take to preventing that problem: designing more continuous and linear metrics, creating larger test sets, and sampling more large models.
The paper doesn’t say “emergence isn’t a thing, nothing to worry about here,” despite the provocative title, it gestures toward approaches we can take to make the unpredictable thing more predictable and indicates that the current unpredictability is largely resolved through different metrics, which is exactly what we should be trying to do when we want to avoid dangerous capabilities.
Interesting stuff. The nonlinearity of requiring long sequences of tokens doesn’t seem to be a fatal objection to measuring long sequences, because often we’re interested in capabilities that really do require getting long sequences all correct. But from the perspective of predicting capabilities, this is definitely a point for team straight lines on graphs.
Jason Wei responded at https://www.jasonwei.net/blog/common-arguments-regarding-emergent-abilities.
My thoughts: It is true that some metrics increase smoothly and some don’t. The issue is that some important capabilities are inherently all-or-nothing, and we haven’t yet found surrogate metrics which increase smoothly and correlate with things we care about.
What we want is: for a given capability, predicting whether this capability happens in the model that is being trained.
If extrapolating a smoothly increasing surrogate metric can do that, then emergence of that capability is indeed a mirage. Otherwise, Betteridge’s law of headlines applies.