9 citations · 9 across the 4 of their papers we have counts for
4 papers
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley +2
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited r…
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns
Zhongbin Xie, Vid Kocijan, Thomas Lukasiewicz +1
Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evalua…
Rationalizing Predictions by Adversarial Information Calibration
Lei Sha, Oana-Maria Camburu, Thomas Lukasiewicz
Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive…
Explaining Chest X-ray Pathologies in Natural Language
Maxime Kayser, Cornelius Emde, Oana-Maria Camburu +3
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medic…