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How do two distributions of texts differ? Humans are slow at answering this, since discovering patterns might require tediously reading through hundreds of samples.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 1911
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 1911
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
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Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition
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Applied regression analysis , volume 326
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Crafting papers on machine learning
Langley, P · 2000
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Learning question classifiers
Li, X. and Roth, D · 2002
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Modeling the effects of epidemics on routinely collected data
Zeng, X. and Wagner, M · 2002
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Pang, B. and Lee, L · 2004
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Content based sms spam filtering
Gómez Hidalgo, J. M., Bringas, G. C., Sánz, E. P., and García, F. C · 2006
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Reading tea leaves: How humans interpret topic models
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J., and Blei, D · 2009
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The tacit dimension
Polanyi, M. and Sen, A · 2009
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Learning word vectors for sentiment analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., and Potts, C · 2011
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Towards sms spam filtering: Results under a new dataset
Almeida, T., Hidalgo, J. M. G., and Silva, T. P · 2013
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Acoustic scene classification: Classifying environments from the sounds they produce
Barchiesi, D., Giannoulis, D., Stowell, D., and Plumbley, M. D · 2015
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A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D · 2015
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Did shakespeare write double falsehood? identifying individuals by creating psychological signatures with text analysis
Boyd, R. L. and Pennebaker, J. W · 2015
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Grounding semantics in olfactory perception
Kiela, D., Bulat, L., and Clark, S · 2015
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Character-level convolutional networks for text classification
Zhang, X., Zhao, J., and LeCun, Y · 2015
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Strategic classification
Hardt, M., Megiddo, N., Papadimitriou, C., and Wootters, M · 2016
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Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
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Learning multi-modal grounded linguistic semantics by playing” i spy”
Thomason, J., Sinapov, J., Svetlik, M., Stone, P., and Mooney, R. J · 2016
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Annotation artifacts in natural language inference data
Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., and Smith, N. A · 2017
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A continuously growing dataset of sentential paraphrases
Lan, W., Qiu, S., He, H., and Xu, W · 2017
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Fairness without demographics in repeated loss minimization
Hashimoto, T., Srivastava, M., Namkoong, H., and Liang, P · 2018
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Stress test evaluation for natural language inference
Naik, A., Ravichander, A., Sadeh, N., Rose, C., and Neubig, G · 2018
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Predictive modeling for odor character of a chemical using machine learning combined with natural language processing
Reformer: The efficient transformer
Kitaev, N., Kaiser, L., and Levskaya, A · 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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Adversarial semantic collisions
Song, C., Rush, A., and Shmatikov, V · 2020
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Flex: Unifying evaluation for few-shot nlp
Bragg, J., Cohan, A., Lo, K., and Beltagy, I · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
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Nozaki, Y. and Nakamoto, T · 2018
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Neural network acceptability judgments
Warstadt, A., Singh, A., and Bowman, S. R · 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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Analyzing polarization in social media: Method and application to tweets on 21 mass shootings
Demszky, D., Garg, N., Voigt, R., Zou, J. Y., Gentzkow, M., Shapiro, J. M., and Jurafsky, D · 2019
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Text as data
Gentzkow, M., Kelly, B., and Taddy, M · 2019
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Testing robustness against unforeseen adversaries
Kang, D., Sun, Y., Hendrycks, D., Brown, T., and Steinhardt, J · 2019
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Gao, T., Fisch, A., and Chen, D · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al · 2021
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Prompt waywardness: The curious case of discretized interpretation of continuous prompts
Khashabi, D., Lyu, S., Min, S., Qin, L., Richardson, K., Singh, S., Welleck, S., Hajishirzi, H., Khot, T., Sabharwal, A., et al · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
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Noisy channel language model prompting for few-shot text classification
Min, S., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., Kim, C., Hesse, C., Jain, S., Kosaraju, V., Saunders, W., et al · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al · 2021
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Learning to retrieve prompts for in-context learning
Rubin, O., Herzig, J., and Berant, J · 2021
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Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L. A., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., Dey, M., BARI, M. S., Xu, C., Thakker, U., Sharma, S. S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N. V., Datta, D., Chang, J., Jiang, M. T.-J., Wang, H., Manica, M., Shen, S., Yong, Z. X., Pandey, H., Bawden, R., Wang, T., Neeraj, T., Rozen, J., Sharma, A., Santilli, A., Févry, T., Fries, J. A., Teehan, R., Biderman, S. R., Gao, L., Bers, T. G. O., Wolf, T., and Rush, A. M · 2021
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Annotators with attitudes: How annotator beliefs and identities bias toxic language detection
Sap, M., Swayamdipta, S., Vianna, L., Zhou, X., Choi, Y., and Smith, N. A · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Li, M., Kim, J. W., Hajishirzi, H., Farhadi, A., Namkoong, H., and Schmidt, L · 2021
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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Zhong, R., Lee, K., Zhang, Z., and Klein, D · 2021
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Natural language descriptions of deep visual features
Hernandez, E., Schwettmann, S., Bau, D., Bagashvili, T., Torralba, A., and Andreas, J · 2022
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Benchmarking generalization via in-context instructions on 1,600+ language tasks
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Arunkumar, A., Ashok, A., Dhanasekaran, A. S., Naik, A., Stap, D., et al · 2022
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