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Large Language Models (LLMs) excel in natural language understanding by capturing hidden semantics in vector space.
Comparing corpora
Kilgarriff, A · 2001
Earlier work this paper cites.
Efficient Intent Detection with Dual Sentence Encoders
Casanueva, I., Temcinas, T., Gerz, D., Henderson, M., and Vulic, I · 2003
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
Dolan, B. and Brockett, C · 2005
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Pang, B. and Lee, L · 2005
Earlier work this paper cites.
The second pascal recognising textual entailment challenge
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., and Szpektor, I · 2006
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Dagan, I., Glickman, O., and Magnini, B · 2006
Earlier work this paper cites.
Differential privacy
Dwork, C · 2006
Earlier work this paper cites.
The third pascal recognizing textual entailment challenge
Giampiccolo, D., Magnini, B., Dagan, I., and Dolan, B · 2007
Earlier work this paper cites.
The fifth pascal recognizing textual entailment challenge
Bentivogli, L., Dagan, I., Dang, H. T., Giampiccolo, D., and Magnini, B · 2009
Earlier work this paper cites.
Accurate estimation of the degree distribution of private networks
Hay, M., Li, C., Miklau, G., and Jensen, D · 2009
Earlier work this paper cites.
Contributions to the Study of SMS Spam Filtering: New Collection and Results
Almeida, T. A., Hidalgo, J. M. G., and Yamakami, A · 2011
Earlier work this paper cites.
Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Gurulingappa, H., Rajput, A. M., Roberts, A., Fluck, J., Hofmann-Apitius, M., and Toldo, L · 2012
Earlier work this paper cites.
Broadening the scope of differential privacy using metrics
Chatzikokolakis, K., Andrés, M., Bordenabe, N., and Palamidessi, C · 2013
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
McAuley, J. and Leskovec, J · 2013
Earlier work this paper cites.
The geometry of differential privacy
Nikolov, A., Talwar, K., and Zhang, L · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., and Takala, P · 2014
Earlier work this paper cites.
Character-level Convolutional Networks for Text Classification
Zhang, X., Zhao, J. J., and LeCun, Y · 2015
Earlier work this paper cites.
Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
Earlier work this paper cites.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Earlier work this paper cites.
On the kozachenko–leonenko entropy estimator
Delattre, S. and Fournier, N · 2017
Earlier work this paper cites.
Software applications user reviews
Grano, G., Di Sorbo, A., Mercaldo, F., Visaggio, C. A., Canfora, G., and Panichella, S · 2017
Earlier work this paper cites.
Dailydialog: A manually labelled multi-turn dialogue dataset
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., and Niu, S · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Bolt-on differential privacy for scalable stochastic gradient descent-based analytics, 2017
Wu, X., Li, F., Kumar, A., Chaudhuri, K., Jha, S., and Naughton, J. F · 2017
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising, 2018
Balle, B. and Wang, Y.-X · 2018
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Quora question pairs, 2018
Chen, Z., Zhang, H., Zhang, X., and Zhao, L · 2018
Cited alongside, same era.
Hate Speech Dataset from a White Supremacy Forum
de Gibert, O., Perez, N., García-Pablos, A., and Cuadros, M · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
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., and Liu, P. J · 2020
Later among the works it cites.
Investigating Societal Biases in a Poetry Composition System, 2020
Sheng, E. and Uthus, D · 2020
Later among the works it cites.
Gender classification using twitter text data
Vashisth, P. and Meehan, K · 2020
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Representations of language varieties are reliable given corpus similarity measures
Dunn, J. E · 2021
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Learning and evaluating a differentially private pre-trained language model
Hoory, S., Feder, A., Tendler, A., Cohen, A., Erell, S., Laish, I., Nakhost, H., Stemmer, U., Benjamini, A., Hassidim, A., and Matias, Y · 2021
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents, 2018
Gupta, O. and Raskar, R · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2018
Cited alongside, same era.
Split learning for health: Distributed deep learning without sharing raw patient data, 2018
Vepakomma, P., Gupta, O., Swedish, T., and Raskar, R · 2018
Cited alongside, same era.
Generalised differential privacy for text document processing
Fernandes, N., Dras, M., and McIver, A · 2019
Cited alongside, same era.
Privacy- and utility-preserving textual analysis via calibrated multivariate perturbations, 2019
Feyisetan, O., Balle, B., Drake, T., and Diethe, T · 2019
Cited alongside, same era.
Openwebtext corpus
Gokaslan*, A., Cohen*, V., Pavlick, E., and Tellex, S · 2019
Cited alongside, same era.
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al · 2021
Later among the works it cites.
The-x: Privacy-preserving transformer inference with homomorphic encryption
Chen, T., Bao, H., Huang, S., Dong, L., Jiao, B., Jiang, D., Zhou, H., Li, J., and Wei, F · 2022
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An efficient industrial federated learning framework for aiot: a face recognition application
Ding, Y., Wu, X., Li, Z., Wu, Z., Tan, S., Xu, Q., Pan, W., and Yang, Q · 2022
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Bounding training data reconstruction in private (deep) learning
Guo, C., Karrer, B., Chaudhuri, K., and van der Maaten, L · 2022
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Iron: Private inference on transformers
Hao, M., Li, H., Chen, H., Xing, P., Xu, G., and Zhang, T · 2022
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Denoising differential privacy in split learning
Xu, H., Dutta, A., Liu, W., Li, X., and Kalnis, P · 2022
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Differentially private label protection in split learning
Yang, X., Sun, J., Yao, Y., Xie, J., and Wang, C · 2022
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Hide and seek (has): A lightweight framework for prompt privacy protection, 2023
Chen, Y., Li, T., Liu, H., and Yu, Y · 2023
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Dp-forward: Fine-tuning and inference on language models with differential privacy in forward pass
Du, M., Yue, X., Chow, S. S., Wang, T., Huang, C., and Sun, H · 2023
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Flocks of stochastic parrots: Differentially private prompt learning for large language models, 2023
Duan, H., Dziedzic, A., Papernot, N., and Boenisch, F · 2023
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Protecting user privacy in remote conversational systems: A privacy-preserving framework based on text sanitization, 2023
Kan, Z., Qiao, L., Yu, H., Peng, L., Gao, Y., and Li, D · 2023
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Privacy-preserving prompt tuning for large language model services, 2023
Li, Y., Tan, Z., and Liu, Y · 2023
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Llms can understand encrypted prompt: Towards privacy-computing friendly transformers
Liu, X. and Liu, Z · 2023
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Analyzing leakage of personally identifiable information in language models
Lukas, N., Salem, A., Sim, R., Tople, S., Wutschitz, L., and Zanella-Béguelin, S · 2023
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A split-and-privatize framework for large language model fine-tuning
Shen, X., Liu, Y., Liu, H., Hong, J., Duan, B., Huang, Z., Mao, Y., Wu, Y., and Wu, D · 2023
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Delving into differentially private Transformer
Ding, Y., Wu, X., Meng, Y., Luo, Y., Wang, H., and Pan, W · 2024
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Openfedllm: Training large language models on decentralized private data via federated learning
Ye, R., Wang, W., Chai, J., Li, D., Li, Z., Xu, Y., Du, Y., Wang, Y., and Chen, S · 2024
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