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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , and 440 other author(s). CoRR, (2022)Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, , , , , , , , and . Advances in Neural Information Processing Systems, 35, page 24824--24837. Curran Associates, Inc., (2022)Finetuned Language Models are Zero-Shot Learners., , , , , , , , and . ICLR, OpenReview.net, (2022)Finetuned Language Models Are Zero-Shot Learners., , , , , , , , and . CoRR, (2021)GLaM: Efficient Scaling of Language Models with Mixture-of-Experts., , , , , , , , , and 17 other author(s). CoRR, (2021)Emergent Abilities of Large Language Models., , , , , , , , , and 6 other author(s). Trans. Mach. Learn. Res., (2022)A Framework for Unsupervised Spam Detection in Social Networking Sites., , and . ECIR, volume 7224 of Lecture Notes in Computer Science, page 364-375. Springer, (2012)Scaling Up Models and Data with t5x and seqio., , , , , , , , , and 35 other author(s). J. Mach. Learn. Res., (2023)Chain of Thought Prompting Elicits Reasoning in Large Language Models, , , , , , and . (2022)cite arxiv:2201.11903.Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , and 440 other author(s). Trans. Mach. Learn. Res., (2023)