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An empirical analysis of compute-optimal large language model training., , , , , , , , , and 12 other author(s). NeurIPS, (2022)Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension., , , , and . Trans. Assoc. Comput. Linguistics, (2020)Training datasets for machine reading comprehension and their limitations.. University College London, UK, (2020)British Library, EThOS.Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation., , , , , , , and . EMNLP/IJCNLP (1), page 4081-4091. Association for Computational Linguistics, (2019)Challenges in Detoxifying Language Models., , , , , , , , , and . EMNLP (Findings), page 2447-2469. Association for Computational Linguistics, (2021)Towards Verified Robustness under Text Deletion Interventions., , , , , , and . ICLR, OpenReview.net, (2020)Casting Random Forests as Artificial Neural Networks (and Profiting from It).. GCPR, volume 8753 of Lecture Notes in Computer Science, page 765-771. Springer, (2014)Training Compute-Optimal Large Language Models, , , , , , , , , and 12 other author(s). (2022)UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)., , , , and . FEVER@EMNLP, page 97-102. Association for Computational Linguistics, (2018)Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation., , , , , , , , , and 13 other author(s). CoRR, (2023)