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GPT-3 can perform numerous tasks when provided a natural language prompt that contains a few training examples.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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The ATIS spoken language systems pilot corpus
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Building a question answering test collection
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The PASCAL recognising textual entailment challenge
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How can we know when language models know?
Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2012
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A conversational movie search system based on conditional random fields
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Recursive deep models for semantic compositionality over a sentiment treebank
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Human-level concept learning through probabilistic program induction
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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A diversity-promoting objective function for neural conversation models
Li, J., Galley, M., Brockett, C., Gao, J., and Dolan, B · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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The effect of different writing tasks on linguistic style: A case study of the ROC story cloze task
Schwartz, R., Sap, M., Konstas, I., Zilles, L., Choi, Y., and Smith, N. A · 2017
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Sharp nearby, fuzzy far away: How neural language models use context
Khandelwal, U., He, H., Qi, P., and Jurafsky, D · 2018
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A deep reinforced model for abstractive summarization
Paulus, R., Xiong, C., and Socher, R · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I · 2018
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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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Studying the inductive biases of RNNs with synthetic variations of natural languages
Ravfogel, S., Goldberg, Y., and Linzen, T · 2019
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SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S · 2019
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Learning and evaluating general linguistic intelligence
Yogatama, D., d’Autume, C. d. M., Connor, J., Kocisky, T., Chrzanowski, M., Kong, L., Lazaridou, A., Ling, W., Yu, L., Dyer, C., et al · 2019
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Calibration, entropy rates, and memory in language models
Braverman, M., Chen, X., Kakade, S., Narasimhan, K., Zhang, C., and Zhang, Y · 2020
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COMET: Commonsense transformers for automatic knowledge graph construction
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The CommitmentBank: Investigating projection in naturally occurring discourse
de Marneffe, M.-C., Simons, M., and Tonhauser, J · 2019
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Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
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Language models as knowledge bases?
Petroni, F., Rocktäschel, T., Lewis, P., Bakhtin, A., Wu, Y., Miller, A. H., and Riedel, S · 2019
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How can we know what language models know?
Jiang, Z., Xu, F. F., Araki, J., and Neubig, G
Cited in the paper.
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
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Making pre-trained language models better few-shot learners
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The curious case of neural text degeneration
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It’s not just size that matters: Small language models are also few-shot learners
Schick, T. and Schütze, H · 2020
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AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
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Exploiting cloze questions for few-shot text classification and natural language inference
Schick, T. and Schütze, H · 2021
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