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The Generative AI Paradox: "What It Can Create, It May Not Understand".

P. West, X. Lu, N. Dziri, F. Brahman, L. Li, J. Hwang, L. Jiang, J. Fisher, A. Ravichander, K. Chandu, B. Newman, P. Koh, A. Ettinger, and Y. Choi. CoRR, (2023)

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CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation.A. Ravichander, M. Gardner, and A. Marasovic. EMNLP, page 8729-8755. Association for Computational Linguistics, (2022)Understanding How to Inform Blind and Low-Vision Users about Data Privacy through Privacy Question Answering Assistants.Y. Feng, A. Ravichander, Y. Yao, S. Zhang, R. Chen, S. Wilson, and N. Sadeh. CoRR, (2023)Exploring and Improving the Accessibility of Data Privacy-related Information for People Who Are Blind or Low-vision.Y. Feng, A. Ravichander, Y. Yao, S. Zhang, and N. Sadeh. CoRR, (2022)Investigating the "Wisdom of Crowds" at Scale.A. Mysore, V. Yaligar, I. Ibarra, C. Simoiu, S. Goel, R. Arvind, C. Sumanth, A. Srikantan, B. HS, M. Pahadia and 50 other author(s). UIST (Adjunct Volume), page 75-76. ACM, (2015)Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?A. Ravichander, A. Black, T. Norton, S. Wilson, and N. Sadeh. ACL/IJCNLP (1), page 4125-4140. Association for Computational Linguistics, (2021)On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT.A. Ravichander, E. Hovy, K. Suleman, A. Trischler, and J. Cheung. *SEM@COLING, page 88-102. Association for Computational Linguistics, (2020)The Generative AI Paradox: "What It Can Create, It May Not Understand".P. West, X. Lu, N. Dziri, F. Brahman, L. Li, J. Hwang, L. Jiang, J. Fisher, A. Ravichander, K. Chandu and 4 other author(s). CoRR, (2023)MAPS: Scaling Privacy Compliance Analysis to a Million Apps.S. Zimmeck, P. Story, D. Smullen, A. Ravichander, Z. Wang, J. Reidenberg, N. Russell, and N. Sadeh. Proc. Priv. Enhancing Technol., 2019 (3): 66-86 (2019)How Would You Say It? Eliciting Lexically Diverse Dialogue for Supervised Semantic Parsing.A. Ravichander, T. Manzini, M. Grabmair, G. Neubig, J. Francis, and E. Nyberg. SIGDIAL Conference, page 374-383. Association for Computational Linguistics, (2017)An Empirical Study of Self-Disclosure in Spoken Dialogue Systems.A. Ravichander, and A. Black. SIGDIAL Conference, page 253-263. Association for Computational Linguistics, (2018)

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The Generative AI Paradox: "What It Can Create, It May Not Understand".

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML The Generative AI Paradox: "What It Can Create, It May Not Understand".

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The Generative AI Paradox: "What It Can Create, It May Not Understand".