Lukas Finnveden

Previously "Lanrian" on here. Research analyst at Open Philanthropy. Views are my own.


Extrapolating GPT-N performance

Wiki Contributions


Even just priors on how large effect sizes of interventions are feels like it brings it under 10x unless there are more detailed arguments given for 10x, but I'll give some more specific thoughts below.

Hm, at the scale of "(inter-)national policy", I think you can get quite large effect sizes. I don't know large the effect-sizes of the following are, but I wouldn't be surprised by 10x or greater for:

  • Regulation of nuclear power leading to reduction in nuclear-related harms. (Compared to a very relaxed regulatory regime.)
  • Regulation of pharmaceuticals leading to reduced side-effects from drugs. (Compared to a regime where people can mostly sell what they want, and drugs only get banned after people notice that they're causing harm.)
  • Worker protection standards. (Wikipedia claims that the Netherlands has a ~17x lower rate of fatal workplace accidents than the US, which is ~22x lower than India.) I don't know what's driving the differences here, but the difference between the US and Netherlands suggests that it's not all "individuals can afford to take lower risks in richer countries".

Are you thinking about exploration hacking, here, or gradient hacking as distinct from exploration hacking?

Instead, ARC explicitly tries to paint the moratorium folks as "extreme".

Are you thinking about this post? I don't see any explicit claims that the moratorium folks are extreme. What passage are you thinking about?

Cool paper!

I'd be keen to see more examples of the paraphrases, if you're able to share. To get a sense of the kind of data that lets the model generalize out of context. (E.g. if it'd be easy to take all 300 paraphrases of some statement (ideally where performance improved) and paste in a google doc and share. Or lmk if this is on github somewhere.)

I'd also be interested in experiments to determine whether the benefit from paraphrases is mostly fueled by the raw diversity, or if it's because examples with certain specific features help a bunch, and those occasionally appear among the paraphrases. Curious if you have a prediction about that or if you already ran some experiments that shed some light on this. (I could have missed it even if it was in the paper.)

Here's a proposed operationalization.

For models that can't gradient hack: The model is "capable of doing X" if it would start doing X upon being fine-tuned to do it using a hypothetical, small finetuning dataset that demonstrated how to do the task. (Say, at most 1000 data points.)

(The hypothetical fine-tuning dataset should be a reasonable dataset constructed by a hypothetical team of human who knows how to do the task but aren't optimizing the dataset hard for ideal gradient updates to this particular model, or anything like that.)

For models that might be able to gradient-hack, but are well-modelled as having certain goals: The model is "capable of doing X" if it would start doing X if doing X was a valuable instrumental goal, for it.

For both kinds: "you can get it to do X" if you could make it do X with some large amount of research+compute budget (say, 1% of the pre-training budget), no-holds-barred.

Edit: Though I think your operationalization also looks fine. I mainly wanted to point out that the "finetuning" definition of "capable of doing X" might be ok if you include the possibility of finetuning on hypothetical datasets that we don't have access to. (Since we only know how to check the task — not perform it.)

I intend to write a lot more on the potential “brains vs brawns” matchup of humans vs AGI. It’s a topic that has received surprisingly little depth from AI theorists.

I recommend checking out part 2 of Carl Shulman's Lunar Society podcast for content on how AGI could gather power and take over in practice.

Yeah, I also don't feel like it teaches me anything interesting.

Note that B is (0.2,10,−1)-distinguishable in P.

I think this isn't right, because definition 3 requires that sup_s∗ {B_P− (s∗)} ≤ γ.

And for your counterexample, s* = "C" will have B_P-(s*) be 0 (because there's 0 probably of generating "C" in the future). So the sup is at least 0 > -1.

(Note that they've modified the paper, including definition 3, but this comment is written based on the old version.)

Are you mainly interested in evaluating deceptive capabilities? I.e., no-holds-barred, can you elicit competent deception (or sub-components of deception) from the model? (Including by eg fine-tuning on data that demonstrates deception or sub-capabilities.)

Or evaluating inductive biases towards deception? I.e. testing whether the model is inclined towards deception in cases when the training data didn't necessarily require deceptive behavior.

(The latter might need to leverage some amount of capability evaluation, to distinguish not being inclined towards deception from not being capable of deception. But I don't think the reverse is true.)

Or if you disagree with that way of cutting up the space.

I assume that's from looking at the GPT-4 graph. I think the main graph I'd look at for a judgment like this is probably the first graph in the post, without PaLM-2 and GPT-4. Because PaLM-2 is 1-shot and GPT-4 is just 4 instead of 20+ benchmarks.

That suggests 90% is ~1 OOM away and 95% is ~3 OOMs away.

(And since PaLM-2 and GPT-4 seemed roughly on trend in the places where I could check them, probably they wouldn't change that too much.)

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