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“You may not instantly see why I bring the subject up, but that is because my mind works so phenomenally fast, and I am at a rough estimate thirty billion times more intelligent than you. Let me give you an example. Think of a number, any number.”
“Er, five,” said the mattress.
“Wrong,” said Marvin. “You see?”
― Douglas Adams, Life, the Universe and Everything
The mattress? Like for sleeping?
Yep! The hitchhikers books are so much fun lol
I still think one of my favorite lines is "the ships hung in the sky in much the same way that bricks don't."
37 is well represented. Proof that we've taught AI some of our own weird biases.
What's special about 37? Just that it's prime or is there a superstition or pop culture reference I don't know?
If you discount the pop-culture numbers (for us 7, 42, and 69) its the number most often chosen by people if you ask them for a random number between 1 and 100. It just seems the most random one to choose for a lot of people. Veritasium just did a video about it.
37 is my favorite, because 3x7x37=777 (three sevens), and I think that's neat.
What about 57
I'm curious about that too. Something is twisting weights for 57 fairly strongly in the model but I'm not show what. Maybe its been trained on a bunch of old Heinz 57 varieties marketing.
Probably just because it's prime. It's just that humans are terrible at understanding the concept of randomness. A study by Theodore P. Hill showed that when tasked to pick a random number between 1 and 10, almost a third of the subjects (n was over 8500) picked 7. 10 was the least picked number (if you ditch the few idiots that picked 0).
Why would that need to be proven? We're the sample data. It's implied.
The correctness of the sampling process still needs a proof. Like this.
holy crap, the answer to life the universe and everything XD
What does "temperature" on the Y-axis refer to?
I'm not a hundred percent sure, but afaik it has to do with how random the output of the GPT model will be. At 0 it will always pick the most probable next continuation of a piece of text according to its own prediction. The higher the temperature, the more chance there is for less probable outputs to get picked. So it's most likely to pick 42, but as the temperature increases you see the chance of (according to the model) less likely numbers increase.
This is how temperature works in the softmax function, which is often used in deep learning.
I mean... they didn't specify it had to be random (or even uniform)? But yeah, it's a good showcase of how GPT acquired the same biases as people, from people..
uniform
Reminds me of my previous job where our LLM was grading things too high. The AI "engineer" adjusted the prompt to tell the LLM that the average output should be 3. I had a hard time explaining that wouldn't do anything at all, because all the chats were independent events.
Anyways, I quit that place and the project completely derailed.
Ask humans the same and most common numer is 37
I saw that YouTube video as well.
HA, funny that this comes up. DND Beyond doesn't have a d100, so I opened my ChatGPT sub and had it roll a d100 for me a few times so I could use my magic beans properly.
I use the percentile die for that.
Also an excellent method.
Opened up DND Beyond to check since i remember rolling it before and its there, its between D8 and D10, the picture shows 2 dice
LMs aren't thinking, aren't inventing, they are predicting what is supposed to be answered next, so it's expected that they will produce the same results every time
This graph actually shows a little more about what's happening with the randomness or "temperature" of the LLM.
It's actually predicting the probability of every word (token) it knows of coming next, all at once.
The temperature then says how random it should be when picking from that list of probable next words. A temperature of 0 means it always picks the most likely next word, which in this case ends up being 42.
As the temperature increases, it gets more random (but you can see it still isn't a perfect random distribution with a higher temperature value)
Which model?
When I tried on ChatGPT 4, it wrote a short python script and executed it to get a random integer.
import random
# Pick a random number between 1 and 100
random_number = random.randint(1, 100)
random_number
Only 1000 times? It's interesting that there's such a bias there but it's a computer. Ask it 100,000 times and make sure it's not a fluke.
42, 47, and 50 all make sense to me. What’s the significance of 37, 57, and 73?
There's a great Veritasium video recently about this exact thing: https://youtu.be/d6iQrh2TK98
It's a human thing, though. This is just more evidence of LLM's problem with garbage in, garbage out: it's human biases being present in a system that people want to claim doesn't have them.
People do mention Veritasium, though he doesn't give any significant explanation of the phenomenon.
I still wonder about 47. In Veritasium plots, all these numbers provide a peak, but not 47. I recall from my childhood that I indeed used to notice that number everywhere, but idk why.
NEEDS MOAR 69 FELLOW HUMAN