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Large language models record impressive performance on many natural language processing tasks.
S2ORC: The semantic scholar open research corpus
Lo, K.; Wang, L. L.; Neumann, M.; Kinney, R.; and Weld, D. S. 2019 · 1911
Earlier work this paper cites.
Knowledge guided text retrieval and reading for open domain question answering
Min, S.; Chen, D.; Zettlemoyer, L.; and Hajishirzi, H. 2019 · 1911
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Karpukhin, V.; Oğuz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2004
Earlier work this paper cites.
Knowledge-aided open-domain question answering
Zhou, M.; Shi, Z.; Huang, M.; and Zhu, X. 2020 · 2006
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2020 · 2009
Earlier work this paper cites.
Distilling knowledge from reader to retriever for question answering
Izacard, G.; and Grave, E. 2020 · 2012
Earlier work this paper cites.
Mctest: A challenge dataset for the open-domain machine comprehension of text
Richardson, M.; Burges, C. J.; and Renshaw, E. 2013 · 2013
Earlier work this paper cites.
Race: Large-scale reading comprehension dataset from examinations
Lai, G.; Xie, Q.; Liu, H.; Yang, Y.; and Hovy, E. 2017 · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P.; Cowhey, I.; Etzioni, O.; Khot, T.; Sabharwal, A.; Schoenick, C.; and Tafjord, O. 2018 · 2018
Earlier work this paper cites.
REALM: Retrieval-Augmented Language Model Pre
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M.-w. 2020 · 2020
Earlier work this paper cites.
Heterogeneous graph transformer
Hu, Z.; Dong, Y.; Wang, K.; and Sun, Y. 2020 · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; et al. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Cited alongside, same era.
Unsupervised dense information retrieval with contrastive learning
Izacard, G.; Caron, M.; Hosseini, L.; Riedel, S.; Bojanowski, P.; Joulin, A.; and Grave, E. 2021 · 2021
Cited alongside, same era.
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Paranjape, A.; Khattab, O.; Potts, C.; Zaharia, M.; and Manning, C. D. 2021 · 2021
A survey on retrieval-augmented text generation
Li, H.; Su, Y.; Cai, D.; Wang, Y.; and Liu, L. 2022 · 2022
Later among the works it cites.
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
Singh, A.; D’Arcy, M.; Cohan, A.; Downey, D.; and Feldman, S. 2022 · 2022
Later among the works it cites.
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
De Jong, M.; Zemlyanskiy, Y.; FitzGerald, N.; Ainslie, J.; Sanghai, S.; Sha, F.; and Cohen, W. W. 2023 · 2023
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Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory
Hu, Z.; Iscen, A.; Sun, C.; Wang, Z.; Chang, K.-W.; Sun, Y.; Schmid, C.; Ross, D. A.; and Fathi, A. 2023 · 2023
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Augmented Large Language Models with Parametric Knowledge Guiding
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End-to-end training of multi-document reader and retriever for open-domain question answering
Singh, D.; Reddy, S.; Hamilton, W.; Dyer, C.; and Yogatama, D. 2021 · 2021
Cited alongside, same era.
Kg-fid: Infusing knowledge graph in fusion-in-decoder for open-domain question answering
Yu, D.; Zhu, C.; Fang, Y.; Yu, W.; Wang, S.; Xu, Y.; Ren, X.; Yang, Y.; and Zeng, M. 2021 · 2021
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Improving language models by retrieving from trillions of tokens
Borgeaud, S.; Mensch, A.; Hoffmann, J.; Cai, T.; Rutherford, E.; Millican, K.; Van Den Driessche, G. B.; Lespiau, J.-B.; Damoc, B.; Clark, A.; et al. 2022 · 2022
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Few-shot learning with retrieval augmented language models
Izacard, G.; Lewis, P.; Lomeli, M.; Hosseini, L.; Petroni, F.; Schick, T.; Dwivedi-Yu, J.; Joulin, A.; Riedel, S.; and Grave, E. 2022 · 2022
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Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Khattab, O.; Santhanam, K.; Li, X. L.; Hall, D.; Liang, P.; Potts, C.; and Zaharia, M. 2022 · 2022
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Luo, Z.; Xu, C.; Zhao, P.; Geng, X.; Tao, C.; Ma, J.; Lin, Q.; and Jiang, D. 2023 · 2023
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Munikoti, S.; Acharya, A.; Wagle, S.; and Horawalavithana, S. 2023 · 2023
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Replug: Retrieval-augmented black-box language models
Shi, W.; Min, S.; Yasunaga, M.; Seo, M.; James, R.; Lewis, M.; Zettlemoyer, L.; and Yih, W.-t. 2023 · 2023
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Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering
Wang, Y.; Ma, X.; and Chen, W. 2023 · 2023
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Large Language Models for Information Retrieval: A Survey
Zhu, Y.; Yuan, H.; Wang, S.; Liu, J.; Liu, W.; Deng, C.; Dou, Z.; and Wen, J.-R. 2023 · 2023
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