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

While performing abest-first search, compilation errors constitute 88.7% ofprogramfailures.

However, text-davinci-002 is able to benefit more substantially. We further show that explanations generated by the LLMs may not entail the models' predictions norbefactually grounded intheinput, evenonsimple tasks with extractive explanations. However, these flawed explanations can still be useful as a way to verify LLMs' predictions post-hoc.

Parsing sentences into syntax trees can benefit downstream applications inNLP.

The labeling scarcity is even amplified when we come to adversarially robust deep learning [9], i.e., to training deep models that are not fooled by maliciously crafted, although imperceivable perturbations.

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.

Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks. However, ACL needs tremendous running time to generate the adversarial variants of all training data, which limits its scalability to large datasets. To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method. RCS does not require label information and searches for an informative subset that minimizes a representational divergence, which is the distance of the representation between natural data and their virtual adversarial variants. The vanilla solution of RCS via traversing all possible subsets is computationally prohibitive. Therefore, we theoretically transform RCS into a surrogate problem of submodular maximization, of which the greedy search is an efficient solution with an optimality guarantee for the original problem. Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the large-scale ImageNet-1K dataset to obtain an effective robust representation via RCS.