Wiki Contributions


larger language models may disappoint you [or, an eternally unfinished draft]

This is the best post about language models I've read in a long time. It's clear how much you have used LMs and grokked the peculiar way they operate. You've touched on many important points which I've wanted to write about or have but with less eloquence. Also I glad you liked my blog :) (

I definitely belong to your “enthusiasts” camp, and I agree your fourth point (loss scaling makes models "smarter" fast enough to matter) is a crux. I won't fully defend that here, but I'll do my own brain dump and share some of the thoughts that came up when reading your post.

Discontinuous jumps in capabilities

One of the reasons for my optimism/concern about scaling is that I do expect discontinuous jumps in capabilities, but not in the way you are arguing against here. I don't think discontinuous jumps will necessarily come from discontinuous improvements of the model's single step inference accuracy (though it may), but from the tasks we need it to do.

I see two big sources of discontinuity in tasks and many tasks contain both. The first is that many tasks are somewhat binary in nature. If you can't do it well enough, you basically can't do it at all. The second is that many tasks happen over multiple inferential steps where small improvements in single step accuracy translate into large changes in multistep capabilities.

The most important binary task is whether or not a model can be amplified under some given amplification strategy. As a particular example, at one OOM the model will not be able to amplification technique because it is too unreliable, even with techniques to make it more robust. Then at one OOM it suddenly will. We can observe it getting closer to this, but it can be difficult to say how close we are without getting deep into the gears of the amplification technique.

As an example of multistep inferential tasks, in some experiments collaborators and I found that larger models are dramatically better at solving math problems in multiple steps ("factored cognition"), while accuracy of solving the problem in a single step increases more continuously. Whether this is counts as a fundamentally new capability depends on your definition, but the pragmatic result is discontinuous competence. (A few of our results were eventually posted here)

We should expect to see this with various multi-token tasks which can only be executed if the model chains together many “correct” inferences. It's still a probabilistic matter, as you say: a small model would succeed with some small probability, and the large model will fail with a small probability. However, when the task requires multiple steps to all be executed correctly, the probability of the small model succeeding at the the task dwindles exponentially, magnifying the difference. The problem is more pronounced when you add feature engineering because it's often the case that irregular errors can be accounted for while frequent errors cannot.

Say the task is about 100 tokens long and for each token GPT-3 outputs an acceptable (non-fatal) prediction 90% of the time. The probability of it successfully completing the task is 0.9^100 = 0.00002656139: near 0. A model whose mistake rate is only 1% would complete the task with probability 0.99^100 = 0.36603234127 – more than one out of 3 times. This can be the difference between total impracticality and a task that can be automated with high accuracy by adding a few extra tricks. A model with 99.9% single-token accuracy succeeds most of the time (~90%). This is of course a simplification of the dynamics, but you get the point.


Mistakes during generation are particularly fatal for GPTs because there's no way to go back on them (unless the prompt introduces a mechanism for doing so). GPT updates on its own mistakes and elevates them to a sort of delusive “certainty” after being appended to the prompt. One way of looking at it is that the “delusions” of GPT simulacra are not the model's fault, but the fault of the autoregressive sampling process which spuriously elevates the model's mere guesses to canonical reality.

As you point out, “mistakes” can be of various types, including ones which aren't really failures of capability, and which we won't expect to go away if models scale. However, I think those problems (GPT isn't trying its best, the prompt is ambiguous, etc.) are difficult but tractable to address and will become more tractable as models scale. More powerful models are amenable to more precise control by many methods, even simple prompt programming and fine tuning. OpenAI's instruct models, for instance, are quite reliable at interpreting single-line imperative instructions “correctly” (that is, attempting to execute the instruction), whereas the base models would react to most single-line context-free instructions chaotically.

I also agree that evaluating GPTs with prompts is actually evaluating the GPT+human system, but I'm optimistic/concerned that given time we will automate the effects of this process (automated prompt programming, filtering, fine tuning in clever ways, embedding in larger systems, etc.), even if somehow we don't find ways to make pretrained LMs themselves more intentionally goal-directed.

Prompt noise and shattered cognition

This is excellently put:

I see no single, stable trove of skills being leveraged here and there as needed. I just see stretches of success and failure at imitating ten thousand different kinds of people, all nearly independent of one another, the products of barely-coupled subsystems.

Here's some simple experimental evidence to support this observation. I found that GPT-3's ability to sort a list of 5 integers was 28% with a 0-shot natural language description of the task, 50% with a 10-shot prompt, and 76% (!) accuracy with 0-shot in the style of python documentation/code.

This case cannot be explained by 'meta-learning' because the more effective prompt contains no additional information about how to solve the task. I think simply claiming GPT-3 has only learned "shallow patterns" is also insufficient because it clearly has learned the deep pattern needed to sort lists of integers like this, it just fails to access this ability under different circumstances. Does the pure natural language description and the few-shot prompt invoke a different and inferior strategy, or an imperfect/corrupted version of the same list-sorting subsystem? (I'd love to know.)

In either case, as you say, GPT does not act like it has a centralized repertoire of skills which determines how well it's able to perform tasks across prompts. This is an important intuition. Everything suggests to me that there is no core, no unified self, whether in terms of agency or capability or even knowledge. Gwern has said that he thinks of GPT-3 as an agent which wants to roleplay accurately; I disagree because I don't perceive anything as coherent or centralized as even a “puppetmaster” or “shapeshifter” that controls or roleplays simulacra. The inability of some simulacra to access knowledge and capabilities that would unambiguously make them better imitations, and which different simulacra can somehow access, contributes to my impression of GPT's subsystem disunity. However, I think there is good reason to expect this to change as these models scale.


Despite my blog post, I do think GPT-3 is capable of “meta-learning” – just that this perspective is often misleading, especially for some tasks like translation. I haven't played with small models enough to say how discontinuous it is, but “meta-learning” seems necessary if any size of GPT should be able to coherently continue most long prompts. The same way GPT-3 “updates" from the task demonstrations, it clearly updates on information in a story prompt, such as the demonstrated personality of the characters, information which reveals(constrains) things about the premise, etc. The few-shot “meta-learning” capability is a special case of its general ability to continue text in the style of its training data; lists of examples are a common feature which constrains the future in systematic ways.

Learning curves

The point about LMs' learning curves looking different than those of humans is very important. The probabilistic competencies exhibited by GPT are quite different from what we see from humans.

One note: Contributing to the apparent discontinuity of human learning is that most humans are much less willing to pronounce on topics they're unsure on than GPTs (autoregressively sampled) are. We usually say/think we don't know even when it would be possible to make a probabilistic guess. That said, I do think the way GPTs learn is fundamentally different than humans, and this causes us to both over and underestimate their capabilities.

You've explained well the differences which result from GPT's incentive to imitate a broad range of disparate patterns. Another (related) difference is that whereas humans tend to build up their understanding of a world by learning “fundamentals” like object permanence first, LMs approach competence through a route which masters “superficial”, “stylistic” patterns first, learning to write in the style of famous authors before mastering object permanence. In your words from another post, it learns to run before learning to walk.

This causes some people to conclude that GPTs learns only shallow patterns. I don't think this is true; I think it only approaches the same “deep” patterns from a different trajectory. A “fake it til you make it” approach – but that doesn't mean it won't eventually “make it”. Looking at GPT-2, I could imagine thinking that however impressive the ability of large language models to write in beautiful and difficult (for a human) styles, basic object permanence will always be a problem. GPT-3 doesn't struggle much with it.


Abstract reasoning

I'm interested in knowing more about your reasons for thinking that little will come of scaled LLMs' abstract reasoning capabilities. None of the above suggests this to me. I wonder if your thoughts have changed since Codex was released after you originally drafted this post.

You said that large language models will be better at abstract reasoning in that it will be easier to get them to spit out text that sounds like it's a product of abstract reasoning (implying, perhaps, that it is in some sense not real abstract reasoning). While I agree that language models are very prone to spit out text that looks superficially more like legitimate abstract reasoning than it is, as they're particularly good at imitating surface patterns of competence, why does this imply that they cannot also learn the “real” patterns? What exactly are the "real" patterns?

Many people dismiss the legitimacy of LMs' reasoning because they just parrot probabilities from the training data. But I know you have seen its capacity for generalization. Given a good prompt as a seed, it often is able to reproduce chains of reasoning and conclusions regarding a completely unprecedented state of affairs exactly as they occurred to me. I considered these thoughts to be abstract reasoning when they happened in my mind. So what is it when GPT-3 can reliably reproduce these thoughts?

How do we apply this to Codex writing code that compiles, providing the instrumental fruits of what, if coming from a human, we would not hesitate to call abstract reasoning?

Human evaluation

I agree with your concerns about human evaluation for reasons of unreliableness, underperformance, risk of bias, etc. but I think you overstate the uselessness of the approach. Despite these very real problems, I have found almost universally that people who have spent considerable time using GPT-3 hands-on understand its capabilities and flaws significantly better than researchers who have only read benchmark and ecological evaluation papers. I will even argue that you cannot understand GPT-3 without using it. 

Non-ecological benchmarks (almost all of them) are really, really bad, and most are actively misleading. Ecological evaluations, though you say they exist, are woefully inadequate for probing general intelligence for its capabilities and limits, especially in their current form. I second your call to improve them.