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(3) seems false.

Related to (2) is that sufficient robustness is possible today but very expensive relative to a taxi service like Uber. E.g. It requires having expensive sensors on the cars, hyper detailed mapping of all roads the car can drive on, and a team of humans who can remotely intervene when cars reach a sufficiently tricky situation.

(I haven't yet read the paper carefully). The main question of interest is: "How well can transformer do RL in-context after being trained to do so?" This paper only considers quite narrow and limited tasks but future work will extend this and iterate on various parts of the setup. How do these results update your belief on the main question of interest? It's possible the result can be explained away (as you suggest) but also that there is some algorithm distillation going on.

This is very valuable. I suggest putting this content on Arxiv (even it's less formal that the typical paper).

It could be useful to look at performance of GPT-3 on foreign languages. We know roughly how long it takes humans to reach a given level at a foreign language. E.g. You might find GPT-3 is at a level on 15 different languages that would take a smart human (say) 30 months to achieve (2 months per language). Foreign languages are just a small fraction of the training data.

A few points:

  1. Current models do pretty well on tricky math problems (Minerva), coding competition problems (AlphaCode), and multiple-choice quizzes at college level (MMLU).
  2. In some ways, the models' ability to learn from data is far superior to humans. For example, models trained mostly on English text are still pretty good at Spanish, while English speakers in parts of the US who hear Spanish (passively) every week of their lives usually retain almost nothing. The same is true for being able to imitate other styles or dialects of English, and for programming languages. (Humans after their earlier years can spend years hearing a foreign language everyday and learn almost nothing! Most people need to make huge efforts to learn.)
  3. RNNs are much worse than transformers at in-context learning. It's not just a difference in generative text quality. See this study by DeepMind: https://twitter.com/FelixHill84/status/1524352818261499911

Very helpful post, thanks!

Are there some meta-level lessons about forecasting a dataset like MATH? IIRC, at the time of these forecasts, the only results were GPT2-finetune and GPT3 few-show (without chain-of-thought and self-consistency). For GPT-2, the accuracy scores were <15% for nearly all subjects and difficulty levels. This may be consistent with GPT-2 either not really understanding questions or being so weak at basic arithmetic that it has no chance for most questions. 

Given that performance was so low and that not many models/setups had been tried, there's reason to have a wider distribution on future results. I would still guess that human expert level scores (>95%) should have had very low probability, but even (say) a score of 80% should have had more than 5% chance. (I realize this is posthoc -- I'm not claiming to have made explicit predictions like this). 

A good source of baserates/priors would be to look at how performance improves on benchmarks after the paper introducing the benchmark. One example that comes to mind is Lambada, where performance went from 7.3% in the initial paper to 49% within a year. It'd be cool for someone to plot data from a bunch of benchmarks. Papers with Code will be very helpful but has some missing data. (We might also expect jumpier performance for math-related tasks because once you can do 2-digit arithmetic or elementary algebra reliably then many problems are opened up). 

There's a new Metaculus question on this. The median for near human-level on the exact set of forecasting questions we used is currently 2026. Another relevant question is how well AI will vs crowdforecasts when predicting new questions (e.g. 2023-2024 questions). I'd be excited for people to do more thinking about how much AI will improve at forecasting in coming years. 

Nice post. I generally recommend looking at the model probabilities or taking multiple samples when evaluating a model. For example, does the model give the answer "Joe" 99% probability or close to 50%?

This is a distribution of math problems GPT-3 wasn't finetuned on. Yet it's able to few-shot generalize and perform well. This is an amazing level of robustness relative to 2018 deep learning systems. I don't see why scaling and access to external tools (e.g. to perform long calculations) wouldn't produce the kind of robustness you have in mind.

I'm somewhat skeptical that models will actually be able to robustly learn these kinds of abstractions with a reasonable amount of scaling

GPT-3 (without external calculators) can do very well on math word problems (https://arxiv.org/abs/2206.02336) that combine basic facts about the world with abstract math reasoning. Why think that the kind of causal reasoning humans do is out of reach of scaling (especially if you allow external calculators)? It doesn't seem different in kind from these math word problems. 
 

when can/do foundation models internalize explicitly stated knowledge

Some human causal reasoning is explicit. Humans can't do complex and exact calculations using System 1 intuition, and neither can we do causal reasoning of any sophistication using System 1. The prior over causal relations (e.g. that without looking at any data 'smoking causes cancer' is way more likely than the reverse) is more about general world-model building, and maybe there's more uncertainty about how well scaling learns that.

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