263 citations · 694 across the 35 of their papers we have counts for
6 papers · 1 filter
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan +3
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve…
To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal +3
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natur…
With Little Power Comes Great Responsibility
Dallas Card, Peter Henderson, Urvashi Khandelwal +3
Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored…
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks
Stephen Mussmann, Robin Jia, Percy Liang
Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., of examples are negativ…
Selective Question Answering under Domain Shift
Amita Kamath, Robin Jia, Percy Liang
To avoid giving wrong answers, question answering (QA) models need to know when to abstain from answering. Moreover, users often ask questions that diverge from the model's trainin…
Robust Encodings: A Framework for Combating Adversarial Typos
Erik Jones, Robin Jia, Aditi Raghunathan +1
Despite excellent performance on many tasks, NLP systems are easily fooled by small adversarial perturbations of inputs. Existing procedures to defend against such perturbations ar…