Toshedlighton when change detection is easier than structured learning, we consider testing of edge deletion in forest-structured graphs, and high-temperature ferromagnets as casestudies.

For structured pruning, one promising method is to associate each neuron with a scaling factor, and regularize these scaling factors in training.

Here, we evaluate the few-shot ability ofLMs when such held-out examples are unavailable, a setting we calltrue few-shot learning. We test two model selection criteria, cross-validation and minimum description length, for choosing LM prompts and hyperparameters in the true few-shot setting. Onaverage, both marginally outperform random selection and greatlyunderperform selection basedonheld-out examples.