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Finetuned Language Models Are Zero-Shot Learners., , , , , , , , и . CoRR, (2021)Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models., , , , , , , , , и 10 other автор(ы). ICLR, OpenReview.net, (2024)Promptagator: Few-shot Dense Retrieval From 8 Examples., , , , , , , , , и . ICLR, OpenReview.net, (2023)Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference., , , , , , , , , и 2 other автор(ы). NeurIPS, (2023)Large Dual Encoders Are Generalizable Retrievers., , , , , , , , , и 1 other автор(ы). EMNLP, стр. 9844-9855. Association for Computational Linguistics, (2022)RARR: Researching and Revising What Language Models Say, Using Language Models., , , , , , , , , и 1 other автор(ы). ACL (1), стр. 16477-16508. Association for Computational Linguistics, (2023)Dialog Inpainting: Turning Documents into Dialogs., , , , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 4558-4586. PMLR, (2022)Mixture-of-Experts with Expert Choice Routing., , , , , , , , , и . NeurIPS, (2022)Scaling Instruction-Finetuned Language Models., , , , , , , , , и 25 other автор(ы). J. Mach. Learn. Res., (2024)Promptagator: Few-shot Dense Retrieval From 8 Examples., , , , , , , , , и . CoRR, (2022)