I asked Claude for 37,500 random names, and it can't stop saying Marcus

"this just in, tool behaves predictably outside of imagined specification"

LLMs aren't random name generators any more than a hammer is a screwdriver.

Ask it to write a script to select a random number, associate that number with an entry in a list of first names, a second random number, and associate that with an entry in a list of second names.

Presto bang-o, you've got a bespoke random name generator.

Stop trying to hammer screws and you'll be 73% of the way to effective construction.

eta: gemini completed "generate 1000 random names in a csv in the form "first name, last name" with a sample list featuring 100 unique names and a python script that I didn't ask for but thought I might like.

and prompting haiku with "generate 1000 unique random names in the format "first name last name" gave me exactly 1000 unique names without a repeat and zero marcus.

In sequence. And then this absurd conversation happened:

> What's the probability of getting 10 random numbers without a repletion?

The probability of getting 10 random numbers from 1 to 10 with no repetition (i.e., all ten numbers are distinct) is exactly 0 — it's impossible.

Here's why:

There are only 10 possible numbers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. You are asking for 10 draws. If there is no repetition allowed, you must get each number exactly once. That means the only possible outcomes are the 10! different permutations of {1,2,3,4,5,6,7,8,9,10}.

But in your actual sequence of requests, you received: 1, 7, 4, 9, 6, 3, 8, 2, 5, 10 → That's exactly one full set of 1 through 10 with no repeats.

So while the a-priori probability of getting ten distinct numbers in ten draws (with uniform random selection and replacement) would be:

P(no repetition) = 10/10 × 9/10 × 8/10 × 7/10 × 6/10 × 5/10 × 4/10 × 3/10 × 2/10 × 1/10 = 10! / 10¹⁰ ≈ 0.00000036288 (about 1 in 2.76 million)

...in this specific conversation history, it did happen. The probability we're looking at after the fact is therefore 1 — it occurred with certainty because we observed it.

Also quite popular in black community.

This is of course entirely expected. You can circumvent it slightly by asking for a long array of names and sampling a randomly chosen element near the end of the list. Say ask for 50 names and use the 41-50th element stochastically.

Not perfect, more expensive, but it helps a little. This works by letting the non-zero temperature of sampler seed the attention randomness, similar to prepending other random tokes (but more in-band)

Asking for arrays of uniform or normally distributed numbers is fun too, you can plot the distributions of the n-th element and watch the distributions converge to something not quite entirely unlike what you asked for.

Often there's some bias between element indices too, eg. if you repeat the experiment a large number of times you will still see even numbered items converge to a different distribution than odd numbered items, especially for early elements. Hence the stochastic averaging trick over the last few elements.