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Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing., , , , , , , and . CoRR, (2023)The Generative AI Paradox: "What It Can Create, It May Not Understand"., , , , , , , , , and 4 other author(s). CoRR, (2023)Generating Sequences by Learning to Self-Correct., , , , , , and . ICLR, OpenReview.net, (2023)Analyzing Commonsense Emergence in Few-shot Knowledge Models., , , , and . AKBC, (2021)Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models., , , , , and . ACL/IJCNLP (1), page 1435-1450. Association for Computational Linguistics, (2021)DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts., , , , , , and . ACL/IJCNLP (1), page 6691-6706. Association for Computational Linguistics, (2021)Twist Decoding: Diverse Generators Guide Each Other., , , , , , , and . EMNLP, page 4909-4923. Association for Computational Linguistics, (2022)Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement., , , , , , , , , and 1 other author(s). CoRR, (2023)MERLOT: Multimodal Neural Script Knowledge Models., , , , , , , and . NeurIPS, page 23634-23651. (2021)Symbolic Knowledge Distillation: from General Language Models to Commonsense Models., , , , , , , , and . NAACL-HLT, page 4602-4625. Association for Computational Linguistics, (2022)