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Training datasets for machine reading comprehension and their limitations.

. University College London, UK, (2020)British Library, EThOS.

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An empirical analysis of compute-optimal large language model training., , , , , , , , , и 12 other автор(ы). NeurIPS, (2022)Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension., , , , и . Trans. Assoc. Comput. Linguistics, (2020)Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation., , , , , , , и . EMNLP/IJCNLP (1), стр. 4081-4091. Association for Computational Linguistics, (2019)Challenges in Detoxifying Language Models., , , , , , , , , и . EMNLP (Findings), стр. 2447-2469. Association for Computational Linguistics, (2021)Training datasets for machine reading comprehension and their limitations.. University College London, UK, (2020)British Library, EThOS.Neural Random Forests., , и . CoRR, (2016)Beat the AI: Investigating Adversarial Human Annotations for Reading Comprehension., , , , и . CoRR, (2020)Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models., , , , , , , , , и 2 other автор(ы). NeurIPS, (2022)Reducing Sentiment Bias in Language Models via Counterfactual Evaluation., , , , , , , , и . EMNLP (Findings), том EMNLP 2020 из Findings of ACL, стр. 65-83. Association for Computational Linguistics, (2020)Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation., , , , , , , , , и 13 other автор(ы). CoRR, (2023)