59 citations · 102 across the 15 of their papers we have counts for
15 papers
Does Prompt Formatting Have Any Impact on LLM Performance?
Jia He, Mukund Rungta, David Koleczek +3
In the realm of Large Language Models (LLMs), prompt optimization is crucial for model performance. Although previous research has explored aspects like rephrasing prompt contexts,…
Improving Interpretability via Explicit Word Interaction Graph Layer
Arshdeep Sekhon, Hanjie Chen, Aman Shrivastava +3
Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns a global interact…
White-box Testing of NLP models with Mask Neuron Coverage
Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer +1
Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a numbe…
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1
Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon +2
Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpr…
Evolving Image Compositions for Feature Representation Learning
Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi +1
Convolutional neural networks for visual recognition require large amounts of training samples and usually benefit from data augmentation. This paper proposes PatchMix, a data augm…