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State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training.
The pascal recognising textual entailment challenge
I. Dagan, O. Glickman, and B. Magnini · 2006
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Character-level convolutional networks for text classification
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RACE: Large-scale ReAding comprehension dataset from examinations
G. Lai, Q. Xie, H. Liu, Y. Yang, and E. Hovy · 2017
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Understanding hidden memories of recurrent neural networks
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu · 2017
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Natural language processing meets journalism
O. Popescu and C. Strapparava · 2017
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Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Nlize: A perturbation-driven visual interrogation tool for analyzing and interpreting natural language inference models
S. Liu, Z. Li, T. Li, V. Srikumar, V. Pascucci, and P.-T. Bremer · 2018
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Progressive data science: Potential and challenges, 2018. doi: 10.48550/ARXIV.1812.08032
C. Turkay, N. Pezzotti, C. Binnig, H. Strobelt, B. Hammer, D. A. Keim, J.-D. Fekete, T. Palpanas, Y. Wang, and F. Rusu · 2018
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GLTR: Statistical detection and visualization of generated text
S. Gehrmann, H. Strobelt, and A. Rush · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
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Explain yourself! leveraging language models for commonsense reasoning
N. F. Rajani, B. McCann, C. Xiong, and R. Socher · 2019
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A multiscale visualization of attention in the transformer model
J. Vig · 2019
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Interfaces for explaining transformer language models
J. Alammar · 2020
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Attention flows: Analyzing and comparing attention mechanisms in language models
J. F. DeRose, J. Wang, and M. Berger · 2020
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The finsim 2020 shared task: Learning semantic representations for the financial domain
I. El Maarouf, Y. Mansar, V. Mouilleron, and D. Valsamou-Stanislawski · 2020
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exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
B. Hoover, H. Strobelt, and S. Gehrmann · 2020
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Qasc: A dataset for question answering via sentence composition
T. Khot, P. Clark, M. Guerquin, P. Jansen, and A. Sabharwal · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. L. IV, E. Wallace, and S. Singh · 2020
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Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2021
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What have language models learned?
A. Pearce · 2021
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True few-shot learning with language models
E. Perez, D. Kiela, and K. Cho · 2021
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Multitask prompted training enables zero-shot task generalization, 2021
V. Sanh, A. Webson, C. Raffel, S. H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T. L. Scao, A. Raja, M. Dey, M. S. Bari, C. Xu, U. Thakker, S. S. Sharma, E. Szczechla, T. Kim, G. Chhablani, N. Nayak, D. Datta, J. Chang, M. T.-J. Jiang, H. Wang, M. Manica, S. Shen, Z. X. Yong, H. Pandey, R. Bawden, T. Wang, T. Neeraj, J. Rozen, A. Sharma, A. Santilli, T. Fevry, J. A. Fries, R. Teehan, S. Biderman, L. Gao, T. Bers, T. Wolf, and A. M. Rush · 2021
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The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
I. Tenney, J. Wexler, J. Bastings, T. Bolukbasi, A. Coenen, S. Gehrmann, E. Jiang, M. Pushkarna, C. Radebaugh, E. Reif, and A. Yuan · 2020
Cited alongside, same era.
Evaluating large language models trained on code
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al · 2021
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Making pre-trained language models better few-shot learners
T. Gao, A. Fisch, and D. Chen · 2021
Cited alongside, same era.
Visqa: X-raying vision and language reasoning in transformers
T. Jaunet, C. Kervadec, R. Vuillemot, G. Antipov, M. Baccouche, and C. Wolf · 2021
Cited alongside, same era.
How many data points is a prompt worth?
T. Le Scao and A. Rush · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Cited alongside, same era.
Datasets: A community library for natural language processing
Q. Lhoest, A. V. del Moral, Y. Jernite, A. Thakur, P. von Platen, S. Patil, J. Chaumond, M. Drame, J. Plu, L. Tunstall, J. Davison, M. Sasko, G. Chhablani, B. Malik, S. Brandeis, T. L. Scao, V. Sanh, C. Xu, N. Patry, A. McMillan-Major, P. Schmid, S. Gugger, C. Delangue, T. Matussière, L. Debut, S. Bekman, P. Cistac, T. Goehringer, V. Mustar, F. Lagunas, A. M. Rush, and T. Wolf · 2021
Cited alongside, same era.
It’s not just size that matters: Small language models are also few-shot learners
T. Schick and H. Schütze · 2021
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LMdiff: A visual diff tool to compare language models
H. Strobelt, B. Hoover, A. Satyanaryan, and S. Gehrmann · 2021
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Dodrio: Exploring transformer models with interactive visualization
Z. J. Wang, R. Turko, and D. H. Chau · 2021
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Do prompt-based models really understand the meaning of their prompts?
A. Webson and E. Pavlick · 2021
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Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
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Calibrate before use: Improving few-shot performance of language models
T. Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh · 2021
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Promptsource: An integrated development environment and repository for natural language prompts
S. H. Bach, V. Sanh, Z.-X. Yong, A. Webson, C. Raffel, N. V. Nayak, A. Sharma, T. Kim, M. S. Bari, T. Fevry, et al · 2022
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Building games and apps entirely through natural language using openai’s code-davinci model
A. Mayne · 2022
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Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
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