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With Little Power Comes Great Responsibility.

, , , , , and . EMNLP (1), page 9263-9274. Association for Computational Linguistics, (2020)

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CoNAL: Anticipating Outliers with Large Language Models., , and . CoRR, (2022)Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?, , , , and . CoRR, (2023)Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA., , , , , and . CoRR, (2020)Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches., , , and . CoRR, (2021)Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants., , , , , and . CoRR, (2021)Dynabench: Rethinking Benchmarking in NLP., , , , , , , , , and 9 other author(s). NAACL-HLT, page 4110-4124. Association for Computational Linguistics, (2021)Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality., , , , and . NAACL-HLT, page 4362-4379. Association for Computational Linguistics, (2021)Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer., , , and . NAACL-HLT, page 1865-1874. Association for Computational Linguistics, (2018)Benchmarking Long-tail Generalization with Likelihood Splits., and . EACL (Findings), page 933-953. Association for Computational Linguistics, (2023)Question Answering Infused Pre-training of General-Purpose Contextualized Representations., , and . ACL (Findings), page 711-728. Association for Computational Linguistics, (2022)