This is not good news, this means that we could end up with a dangerously superintelligent AI just by scaling up the number of parameters, without increasing the amount of training data.
1. Specifically larger than the upper bound on lifetime language input for humans, even assuming 24/7 at max reading speed.
Proof: blind and partially sighted people exist.
The RLHF post-training dataset is vastly smaller than the pretraining set and unlikely to have any particular question contaminated. However we did not check explicitly.
We evaluate functional correctness on a set of 164 handwritten programming problems, which we call the HumanEval dataset. ... __It is important for these tasks to be hand-written, since our models are trained on a large fraction of GitHub, which already contains solutions to problems from a variety of sources.__
Human Eval 2
Prompt:
def truncate_number(number: float) -> float: """ Given a positive floating point number, it can be decomposed into and integer part (largest integer smaller than given number) and decimals (leftover part always smaller than 1). Return the decimal part of the number. >>> truncate_number(3.5) 0.5 """
Solution:
return number % 1.0
Human Eval 4
Prompt:
from typing import List def mean_absolute_deviation(numbers: List[float]) -> float: """ For a given list of input numbers, calculate Mean Absolute Deviation around the mean of this dataset. Mean Absolute Deviation is the average absolute difference between each element and a centerpoint (mean in this case): MAD = average | x - x_mean | >>> mean_absolute_deviation([1.0, 2.0, 3.0, 4.0]) 1.0 """
Solution
mean = sum(numbers) / len(numbers)
return sum(abs(x - mean) for x in numbers) / len(numbers)
LLMs are (lossy) compression systems. So they're great for information retrieval. And a lot of what we consider intelligence (and possibly even creativity) is based on information retrieval. Doesn't mean these things are any less impressive but just a note on how we should be interpreting results and understanding the limitations of our tools. Measuring intelligence is a really difficult thing and we need to be aware that the term isn't universally agreed upon and so people are often talking past one another and also some people are conflating the differences as if they are the same.
> You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.
> In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.
-Michael Crichton
Edit: Found the speech this is from.
https://web.archive.org/web/20190808123852/http://larvatus.c...
Another difference: most corporations will avoid doing illegal stuff if the penalties are large enough: the corporation alignment problem is political. Pretty much no extant AI systems can be instructed in this way: we don't know how to align AIs even in theory.
I hate to do this, but can you enumerate them?