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Knowledge and language understanding of models evaluated through question answering (QA) has been usually studied on static snapshots of knowledge, like Wikipedia.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Kuttler, H., Lewis, M., tau Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D · 2005
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Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E. R., and Mitchell, T. M · 2010
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SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Kudo, T. and Richardson, J · 2012
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SQuAD: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Dynamic evaluation of neural sequence models, 2017
Krause, B., Kahembwe, E., Murray, I., and Renals, S · 2017
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Tempquestions: A benchmark for temporal question answering
Jia, Z., Abujabal, A., Saha Roy, R., Strötgen, J., and Weikum, G · 2018
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The NarrativeQA reading comprehension challenge
Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E · 2018
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Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2018
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Synthetic qa corpora generation with roundtrip consistency, 2019
Alberti, C., Andor, D., Pitler, E., Devlin, J., and Collins, M · 2019
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Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., and Salakhutdinov, R · 2019
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Unified language model pre-training for natural language understanding and generation, 2019
Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., and Hon, H.-W · 2019
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Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Kelcey, M., Devlin, J., Lee, K., Toutanova, K. N., Jones, L., Chang, M.-W., Dai, A., Uszkoreit, J., Le, Q., and Petrov, S · 2019
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Language models are few-shot learners, 2020
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Realm: Retrieval-augmented language model pre-training, 2020
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M.-W · 2020
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Drinking from a firehose: Continual learning with web-scale natural language, 2020
Hu, H., Sener, O., Sha, F., and Koltun, V · 2020
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Leveraging passage retrieval with generative models for open domain question answering, 2020
Izacard, G. and Grave, E · 2020
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Dense passage retrieval for open-domain question answering
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Generalization through memorization: Nearest neighbor language models
Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., and Lewis, M · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Towards continual knowledge learning of language models, 2021
Jang, J., Ye, S., Yang, S., Shin, J., Han, J., Kim, G., Choi, S. J., and Seo, M · 2021
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Complex temporal question answering on knowledge graphs
Jia, Z., Pramanik, S., Saha Roy, R., and Weikum, G · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liška, A., Terzi, T., Gimenez, M., de Masson d’Autume, C., Kočiský, T., Ruder, S., Yogatama, D., Cao, K., Young, S., and Blunsom, P · 2021
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Paq: 65 million probably-asked questions and what you can do with them, 2021
Lewis, P., Wu, Y., Liu, L., Minervini, P., Küttler, H., Piktus, A., Stenetorp, P., and Riedel, S · 2021
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Challenges in generalization in open domain question answering
Liu, L., Lewis, P. S. H., Riedel, S., and Stenetorp, P · 2021
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Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., and Kiela, D · 2020
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TORQUE: A reading comprehension dataset of temporal ordering questions
Ning, Q., Wu, H., Han, R., Peng, N., Gardner, M., and Roth, D · 2020
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On the importance of diversity in question generation for QA
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Question answering over temporal knowledge graphs
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Simple entity-centric questions challenge dense retrievers, 2021
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Archivalqa: A large-scale benchmark dataset for open domain question answering over archival news collections, 2021
Wang, J., Jatowt, A., and Yoshikawa, M · 2021
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SituatedQA: Incorporating extra-linguistic contexts into QA
Zhang, M. and Choi, E · 2021
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