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Large pre-training language models (PLMs) have shown promising in-context learning abilities.
Toward semantics-based answer pinpointing
Hovy, E., Gerber, L., Hermjakob, U., Lin, C.-Y., and Ravichandran, D · 2001
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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Roemmele, M., Bejan, C. A., and Gordon, A. S · 2011
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The winograd schema challenge
Levesque, H. J., Davis, E., and Morgenstern, L · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
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Effective approaches to attention-based neural machine translation
Luong, T., Pham, H., and Manning, C. D · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Joshi, M., Choi, E., Weld, D., and Zettlemoyer, L · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Khashabi, D., Chaturvedi, S., Roth, M., Upadhyay, S., and Roth, D · 2018
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Generating wikipedia by summarizing long sequences
Liu, P. J., Saleh, M., Pot, E., Goodrich, B., Sepassi, R., Kaiser, L., and Shazeer, N · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Williams, A., Nangia, N., and Bowman, S · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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Axial attention in multidimensional transformers
Ho, J., Kalchbrenner, N., Weissenborn, D., and Salimans, T · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A. R., Choi, S., and Teh, Y. W · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., and Auli, M · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Pilehvar, M. T. and Camacho-Collados, J · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Compressive transformers for long-range sequence modelling
Rae, J. W., Potapenko, A., Jayakumar, S. M., Hillier, C., and Lillicrap, T. P · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V · 2019
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ETC: Encoding long and structured inputs in transformers
Ainslie, J., Ontanon, S., Alberti, C., Cvicek, V., Fisher, Z., Pham, P., Ravula, A., Sanghai, S., Wang, Q., and Yang, L · 2020
Cited alongside, same era.
Longformer: The long-document transformer
Beltagy, I., Peters, M. E., and Cohan, A · 2020
Cited alongside, same era.
Language models are few-shot learners
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
Cited alongside, same era.
Language models are few-shot learners
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
Cited alongside, same era.
Nyströmformer: A nyström-based algorithm for approximating self-attention
Xiong, Y., Zeng, Z., Chakraborty, R., Tan, M., Fung, G., Li, Y., and Singh, V · 2021
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GPT-NeoX-20B: An open-source autoregressive language model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., Pieler, M., Prashanth, U. S., Purohit, S., Reynolds, L., Tow, J., Wang, B., and Weinbach, S · 2022
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Prompt injection: Parameterization of fixed inputs
Choi, E., Jo, Y., Jang, J., and Seo, M · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2022
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Gao, L., Biderman, S. R., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., and Leahy, C · 2020
Cited alongside, same era.
Compressive transformers for long-range sequence modelling
Rae, J. W., Potapenko, A., Jayakumar, S. M., Hillier, C., and Lillicrap, T. P · 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., Liu, P. J., et al · 2020
Cited alongside, same era.
Sparse sinkhorn attention
Tay, Y., Bahri, D., Yang, L., Metzler, D., and Juan, D · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Wang, S., Li, B., Khabsa, M., Fang, H., and Ma, H · 2020
Cited alongside, same era.
Big bird: Transformers for longer sequences
Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontañón, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A · 2020
Cited alongside, same era.
Skyformer: Remodel self-attention with gaussian kernel and nystr\”om method
Chen, Y., Zeng, Q., Ji, H., and Yang, Y · 2021
Cited alongside, same era.
Rethinking attention with performers
Choromanski, K. M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlós, T., Hawkins, P., Davis, J. Q., Mohiuddin, A., Kaiser, L., Belanger, D. B., Colwell, L. J., and Weller, A · 2021
Cited alongside, same era.
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., Webson, A., Gu, S. S., Dai, Z., Suzgun, M., Chen, X., Chowdhery, A., Narang, S., Mishra, G., Yu, A., Zhao, V., Huang, Y., Dai, A., Yu, H., Petrov, S., Chi, E. H., Dean, J., Devlin, J., Roberts, A., Zhou, D., Le, Q. V., and Wei, J · 2022
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Efficiently modeling long sequences with structured state spaces
Gu, A., Goel, K., and Re, C · 2022
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Diagonal state spaces are as effective as structured state spaces
Gupta, A., Gu, A., and Berant, J · 2022
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What makes good in-context examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2022
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MetaICL: Learning to learn in context
Min, S., Lewis, M., Zettlemoyer, L., and Hajishirzi, H · 2022
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ABC: Attention with bounded-memory control
Peng, H., Kasai, J., Pappas, N., Yogatama, D., Wu, Z., Kong, L., Schwartz, R., and Smith, N. A · 2022
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Bloom: A 176b-parameter open-access multilingual language model
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., et al · 2022
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Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V. A., Zhang, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B · 2022
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Learning by distilling context
Snell, C., Klein, D., and Zhong, R · 2022
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Transformer memory as a differentiable search index
Tay, Y., Tran, V. Q., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., et al · 2022
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Self-adaptive in-context learning
Wu, Z., Wang, Y., Ye, J., and Kong, L · 2022
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Linear complexity randomized self-attention mechanism
Zheng, L., Wang, C., and Kong, L · 2022
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Efficient attention via control variates
Anonymous · 2023
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A survey for in-context learning
Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J., Li, L., and Sui, Z · 2023
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Efficient attention via control variates
Zheng, L., Yuan, J., Wang, C., and Kong, L · 2023
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