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Multitask Prompted Training Enables Zero-Shot Task Generalization, , , , , , , , , and 30 other author(s). International Conference on Learning Representations, (2022)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, , , , , , , , , and 441 other author(s). (2022)cite arxiv:2206.04615Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench.Crosslingual Generalization through Multitask Finetuning., , , , , , , , , and 9 other author(s). CoRR, (2022)KMMLU: Measuring Massive Multitask Language Understanding in Korean., , , , , , , , and . CoRR, (2024)BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting., , , , , , , , , and 4 other author(s). CoRR, (2022)Can Transformers Learn to Solve Problems Recursively?, , , , and . CoRR, (2023)Large language models are not zero-shot communicators., , , , , and . CoRR, (2022)Multitask Prompted Training Enables Zero-Shot Task Generalization., , , , , , , , , and 31 other author(s). CoRR, (2021)Fooling MOSS Detection with Pretrained Language Models., and . CIKM, page 2933-2943. ACM, (2022)RWKV: Reinventing RNNs for the Transformer Era., , , , , , , , , and 22 other author(s). EMNLP (Findings), page 14048-14077. Association for Computational Linguistics, (2023)