To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.

Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

We then resample a dataset with these domain weights and train a larger, full-sized model.

Presented with class names or unlabeled test samples, Neural Priming enables the model to recall and conditions its parameters on relevant data seen throughout pretraining, thereby priming it for the test distribution. Neural Priming can be performed at inference, even for pretraining datasets as large as LAION-2B. Performing lightweight updates on the recalled data significantly improves accuracy across a variety of distribution shift and transfer learning benchmarks.