26 citations · 27 across the 3 of their papers we have counts for
5 papers
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
Hung-Ting Chen, Michael J. Q. Zhang, Eunsol Choi
Question answering models can use rich knowledge sources -- up to one hundred retrieved passages and parametric knowledge in the large-scale language model (LM). Prior work assumes…
Entity Cloze By Date: What LMs Know About Unseen Entities
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi +1
Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. However, in a dynamic world, new entities constantly arise. We pr…
SituatedQA: Incorporating Extra-Linguistic Contexts into QA
Michael J. Q. Zhang, Eunsol Choi
Answers to the same question may change depending on the extra-linguistic contexts (when and where the question was asked). To study this challenge, we introduce SituatedQA, an ope…
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi +1
Most benchmark datasets targeting commonsense reasoning focus on everyday scenarios: physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al., 201…
Deep Weighted Averaging Classifiers
Dallas Card, Michael Zhang, Noah A. Smith
Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text. Despite these gains, however, c…