Qian Huang

TL;DR: The Holistic Evaluation of Language Models (HELM) as mentioned in this paper ) is a popular benchmark for language models, with 30 models evaluated on 16 core scenarios and 7 metrics, exposing important trade-offs.

TL;DR: This work shows that for many standard transductive node classification benchmarks, it can exceed or match the performance of state-of-the-art GNNs by combining shallow models that ignore the graph structure with two simple post-processing steps that exploit correlation in the label structure.

TL;DR: The Intermediate Level Attack (ILA) is introduced, which attempts to fine-tune an existing adversarial example for greater black-box transferability by increasing its perturbation on a pre-specified layer of the source model, improving upon state-of-the-art methods.

TL;DR: The Intermediate Level Attack (ILA) as mentioned in this paper attempts to fine-tune an existing adversarial example for greater black-box transferability by increasing its perturbation on a pre-specified layer of the source model.

TL;DR: In this paper, the authors combine shallow multilayer perceptrons models with two simple postprocessings for correlation in the label structure: (i) an error correlation that spreads residual errors in training data to correct errors in test data and (ii) prediction correlation that smooths the predictions on the test data.