82 citations · 310 across the 24 of their papers we have counts for
38 papers
Debiasing Methods in Natural Language Understanding Make Bias More Accessible
Michael Mendelson, Yonatan Belinkov
Model robustness to bias is often determined by the generalization on carefully designed out-of-distribution datasets. Recent debiasing methods in natural language understanding (N…
A Generative Approach for Mitigating Structural Biases in Natural Language Inference
Dimion Asael, Zachary Ziegler, Yonatan Belinkov
Many natural language inference (NLI) datasets contain biases that allow models to perform well by only using a biased subset of the input, without considering the remainder featur…
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann +3
Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the…
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson
While large-scale pretrained language models have obtained impressive results when fine-tuned on a wide variety of tasks, they still often suffer from overfitting in low-resource s…
Probing Classifiers: Promises, Shortcomings, and Advances
Yonatan Belinkov
Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is s…
Learning from others' mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov +1
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying…