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Found 6 papers, 4 papers with code
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
Counterfactually Probing Language Identity in Multilingual Models
We use one such technique, AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models (mBERT and XLM-R).
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks.
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior.
How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Current studies of bias in NLP rely mainly on identifying (unwanted or negative) bias towards a specific demographic group.
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability.
