161 citations · 656 across the 43 of their papers we have counts for
55 papers
Coverage-based Example Selection for In-Context Learning
Shivanshu Gupta, Matt Gardner, Sameer Singh
In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informati…
Successive Prompting for Decomposing Complex Questions
Dheeru Dua, Shivanshu Gupta, Sameer Singh +1
Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of…
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation
Abhilasha Ravichander, Matt Gardner, Ana Marasović
The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challengi…
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning
Orion Weller, Kevin Seppi, Matt Gardner
Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to nove…
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Sanjay Subramanian, William Merrill, Trevor Darrell +3
Training a referring expression comprehension (ReC) model for a new visual domain requires collecting referring expressions, and potentially corresponding bounding boxes, for image…
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp +1
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they a…