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QuALITY: Question Answering with Long Input Texts, Yes!

, , , , , , , , , , and . NAACL-HLT, page 5336-5358. Association for Computational Linguistics, (2022)

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LLMs Are Prone to Fallacies in Causal Inference., , , and . CoRR, (2024)QuALITY: Question Answering with Long Input Texts, Yes!, , , , , , , , , and 1 other author(s). NAACL-HLT, page 5336-5358. Association for Computational Linguistics, (2022)Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition., , and . ICASSP, page 8254-8258. IEEE, (2020)Personas as a Way to Model Truthfulness in Language Models., , , , and . CoRR, (2023)Cross-Lingual Training for Automatic Question Generation., , , , and . ACL (1), page 4863-4872. Association for Computational Linguistics, (2019)Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples., , , , , , and . CoRR, (2023)An Investigation of the (In)effectiveness of Counterfactually Augmented Data., and . ACL (1), page 3668-3681. Association for Computational Linguistics, (2022)Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation., , , , and . Trans. Mach. Learn. Res., (2024)Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension., , , and . ACL (1), page 2714-2725. Association for Computational Linguistics, (2019)Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens., , and . EMNLP, page 9804-9817. Association for Computational Linguistics, (2022)