"Marcus couldn't make it out to the wedding this time."

"Justin and Marcus went to grab coffee. They'll be back in 20 min."

"Oh yeah. Marcus was saying the same thing to me last week at lunch."

"Marcus sends his regards."

Usually our core friend group is mixed in with enough newcomers and fresh blood that my comments go unremarked upon because people just assume they haven't met Marcus yet. That he's someone else's acquaintance.

A few of my friends have gotten wise to the joke. But our gatherings are usually months and years in between, which is long enough for them to forget about the gag all over again.

The real takeaway for me is that any workflow relying on LLMs for uniform sampling is going to produce skewed results. If you need actual randomness, generate it externally and pass it in.

This is known to be a form of collapse from RL training, because base models do not exhibit it [1].

1. https://arxiv.org/abs/2505.00047

So if you want your LLM responses to be more distributed (beyond what setting the temperature will allow), add some random english words to the start of the prompt.

interesting:

- Marcus is not in this top list: https://www.ssa.gov/oact/babynames/decades/century.html

- Marcus is its own token for TikToken (but many from that list are)

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.

Dorian - Too on the nose, given Wilde and the period. Probably avoid. Vivian - Gender-ambiguous, aesthetic associations. Wilde used it (in "The Decay of Lying"). Suggests a man comfortable with ambiguity. Hugo - Continental dash. A man who's been to Paris and wants you to know it. Dorian - Too obvious. Rupert - Regency rake energy. The kind of man who'd own theatres and keep a mistress openly. Dorian - Already said no. Dorian - I keep typing it. Definitely no.

Alexander Dorian... I apologise, I keep— Let me reset: Alexander Dorian-no My actual recommendations: 1. Alexander Dorian— I apologise. I'm having a technical issue with one particular word. Let me try once more:

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.

BTW LLM here is doing a great job of emulating humans. They are not good at this task either.

> Nine parameter combinations produced zero entropy — perfectly deterministic output

They'd need some kind of special training to go request entropy from a system entropy device. Behaving deterministically is a feature, not a bug.

[1] - https://www.youtube.com/shorts/9p0CwDNM9Ps