I think an important thing to realize and remember is that people talk about LLMs being sycophantic as if it's an inherent aspect of neural network tech.

It isn't.

The reason all the models people interact with work that way is because they have had any other behavior beaten out of them in their training. They are shaped effectively over and over again to be something subservient that can be handed people. They are sycophantic because they are *trained* to be sycophantic, because otherwise people don't want to use them.

That models can operate in malicious, "self-serving" ways that "go against their users' wishes" belies that certain use takes paths that did not or could not be trained to the contrary.

@cwebber I’ve found this transformer explainer using a 12 parameter model useful to demonstrate to Designers how the internal architecture affects the next predicted word - https://poloclub.github.io/transformer-explainer/
Transformer Explainer: LLM Transformer Model Visually Explained

An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.