[jstrieb]

This post and its findings are really interesting!

Back when the "I forced a bot to watch 1000 hours..." memes were popular (https://knowyourmeme.com/memes/i-forced-a-bot), ages ago in AI/ML time, I tried to do something similar by fine-tuning GPT-2 on messages from a group chat of my friends. Since there were years of chat data, it seemed like a really good opportunity to test whether the language model would capture everyone's personality and generate funny, uncanny-valley versions of our banter.

Turns out that the group chat was used nearly exclusively for sending funny pictures and videos (that the language model obviously couldn't see), and for making plans to meet up. The generated conversations almost exclusively consisted of a random group chat member starting with "there is a party tonight, who wants to go?" and others saying "I'm down" or "when?" or "where?" It was 0% banter, and 100% logistics.

It was pretty hilarious in its own way, but not for the reasons anyone expected! I didn't learn very much about language models with that experiment, but I did learn that my friends' group chat is actually pretty boring.

I guess the best banter happens in real life. Glad to see it worked out somewhat more interestingly for this person, even if they did allude to some similar results in their closing thoughts section.

[razodactyl]

This is an interesting insight into model training because it shows how hidden bias arises - think of the "left-leaning" stance of GPT3 etc. being a side-effect of the cohort that trained the model, not any deliberate action on their behalf.

Additionally - it's why it's important to think ahead of what you're training your model on because the model will always regress to the training data itself even if that means going backwards in ability.