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Context Generation Improves Open Domain Question Answering., , , , , , , , и . EACL (Findings), стр. 781-796. Association for Computational Linguistics, (2023)Adding Instructions during Pretraining: Effective way of Controlling Toxicity in Language Models., , , и . EACL, стр. 2628-2643. Association for Computational Linguistics, (2023)Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models., , , , , , , , и . NeurIPS, (2022)Keynote Talk 2 Training Large Language Models: Challenges and Opportunities.. IPDPS Workshops, стр. 1245. IEEE, (2022)Factuality Enhanced Language Models for Open-Ended Text Generation., , , , , , и . NeurIPS, (2022)Training Question Answering Models From Synthetic Data., , , , и . EMNLP (1), стр. 5811-5826. Association for Computational Linguistics, (2020)MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models., , , , , , и . EMNLP (1), стр. 2831-2845. Association for Computational Linguistics, (2020)End-to-End Training of Neural Retrievers for Open-Domain Question Answering., , , , , , и . ACL/IJCNLP (1), стр. 6648-6662. Association for Computational Linguistics, (2021)Data, Data Everywhere: A Guide for Pretraining Dataset Construction., , , , , , , , и . CoRR, (2024)Evaluating Parameter Efficient Learning for Generation., , , , , , , , и . EMNLP, стр. 4824-4833. Association for Computational Linguistics, (2022)