Generative AI, which is currently riding a crest of popular discourse, promises a world where the simple transforms into the complex—where a simple distribution evolves into intricate patterns of images, sounds, or text, rendering the artificial startlingly real.
The common approach to communicate a large language model’s (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user…
What distinguishes robust models from non-robust ones? While for ImageNet distribution shifts it has been shown that such differences in robustness can be traced back predominantly to differences in training data, so far it is not known what that translates to in terms of what the model has learned. In…