Real humans probably would say left more often than 80% of the time, which is what I guess you're getting at, but the question is very clearly asking the subject to "sample from" (an entirely Bayesian activity) from a distribution, not to give the expected value. GPT4 gives the expected value and this is simply wrong.
Only at T=0. See my edit above how this changes everything.
I think its pretty simple and straightforward: the model simply fails to understand the question and can reasonably be said to not understand probability.
This is a good point. LLMs are bad at this, okay, but humans aren't great at it either.
On the other hand, does it really matter? With a slight tweak to the prompt, ChatGPT generates some serviceable code:
> Run a function to produce a random number between 1 and 10. What is the number?
import random
# Generate a random number between 1 and 10
random_number = random.randint(1, 10)
random_number
The random number generated between 1 and 10 is 9.Okay so are any GPU compilers intentionally introducing data races in programs that previously exhibited no data races?
Here’s a good jumping off point: https://pytorch.org/docs/stable/generated/torch.use_determin...
No, that's not what I meant. Although it would still make more sense than what the author did. The problem lies in the way you actually determine probabilities. We know that humans are bad random number generators, but they understand the concept enough to come up with random looking stuff if you give them the chance. The LLMs here were not even given a chance. In essence, the author is complaining that the LLMs are not behaving according to frequentist statistics when he evaluates them in a strictly Bayesian setting.