But the training corpus also has a lot of stories of people who didn't.

The "but muah training data" thing is increasingly stupid by the year.

For example, in the training data of humans, there's mixed and roughly equal preferences to be the big spoon or little spoon in cuddling.

So why does Claude Opus (both 3 and 4) say it would prefer to be the little spoon 100% of the time on a 0-shot at 1.0 temp?

Sonnet 4 (which presumably has the same training data) alternates between preferring big and little spoon around equally.

There's more to model complexity and coherence than "it's just the training data being remixed stochastically."

The self-attention of the transformer architecture violates the Markov principle and across pretraining and fine tuning ends up creating very nuanced networks that can (and often do) bias away from the training data in interesting and important ways.