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Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks.
M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer, “TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 1601–1611. [Online]. Available: https://aclanthology.org/P17-1147
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R. Zellers, Y. Bisk, R. Schwartz, and Y. Choi, “SWAG: A large-scale adversarial dataset for grounded commonsense inference,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 93–104. [Online]. Available: https://www.aclweb.org/anthology/D18-1009
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Z. Yang et al. , “HotpotQA: A dataset for diverse, explainable multi-hop question answering,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 2018, pp. 2369–2380. [Online]. Available: https://aclanthology.org/D18-1259
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K. Lee, M.-W. Chang, and K. Toutanova, “Latent retrieval for weakly supervised open domain question answering,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 6086–6096. [Online]. Available: https://aclanthology.org/P19-1612
2019
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T. Kwiatkowski et al. , “Natural questions: A benchmark for question answering research,” Transactions of the Association for Computational Linguistics , vol. 7, pp. 452–466, 2019. [Online]. Available: https://aclanthology.org/Q19-1026
2019
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T. Brown et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
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2020
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X. Ho, A.-K. Duong Nguyen, S. Sugawara, and A. Aizawa, “Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps,” in Proceedings of the 28th International Conference on Computational Linguistics . Barcelona, Spain (Online): International Committee on Computational Linguistics, Dec. 2020, pp. 6609–6625. [Online]. Available: https://aclanthology.org/2020.coling-main.580
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L. Ouyang et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
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O. Khattab et al. , “Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp,” 2023
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W. Yu et al. , “Generate rather than retrieve: Large language models are strong context generators,” 2023
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