Most of the time, when we talk about large language models (LLMs), we end up in the weeds of training data and parameter counts. Useful if you’re a researcher; less useful if you’re a leader, policymaker, or practitioner trying to answer a simpler question:
“Is this thing actually behaving in a way I’m comfortable with?”
Two realities make that hard:
The training data is too large for humans to grasp in any meaningful way.
The models are too complex for us to truly understand their internal “decision making.”
But their outputs – the words they put on the page – are something we can read, interrogate, and assess.