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Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it.
Measures of distance between probability distributions
Chung, J., Kannappan, P., Ng, C. T., and Sahoo, P · 1989
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Predicting early psychiatric readmission with natural language processing of narrative discharge summaries
Rumshisky, A., Ghassemi, M., Naumann, T., Szolovits, P., Castro, V., McCoy, T., and Perlis, R · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Balle, B. and Wang, Y.-X · 2018
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2018
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Towards private synthetic text generation
Bommasani, R., Wu, S., and Schofield, X · 2019
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Dong, J., Roth, A., and Su, W. J · 2019
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
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Well-read students learn better: On the importance of pre-training compact models
Turc, I., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations
Feyisetan, O., Balle, B., Drake, T., and Diethe, T · 2020
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Large-scale differentially private bert
Anil, R., Ghazi, B., Gupta, V., Kumar, R., and Manurangsi, P · 2021
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Large language models can be strong differentially private learners
Li, X., Tramer, F., Liang, P., and Hashimoto, T · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Pillutla, K., Swayamdipta, S., Zellers, R., Thickstun, J., Welleck, S., Choi, Y., and Harchaoui, Z · 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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Large scale private learning via low-rank reparametrization
Yu, D., Zhang, H., Chen, W., Yin, J., and Liu, T.-Y · 2021
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Differentially private bias-term only fine-tuning of foundation models
Natural language processing: practical applications in medicine and investigation of contextual autocomplete
Voytovich, L. and Greenberg, C · 2022
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Differentially private fine-tuning of language models
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
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Falcon-40B: an open large language model with state-of-the-art performance
Almazrouei, E., Alobeidli, H., Alshamsi, A., Cappelli, A., Cojocaru, R., Debbah, M., Goffinet, E., Heslow, D., Launay, J., Malartic, Q., Noune, B., Pannier, B., and Penedo, G · 2023
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Tem: High utility metric differential privacy on text
Carvalho, R. S., Vasiloudis, T., Feyisetan, O., and Wang, K · 2023
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Bu, Z., Wang, Y.-X., Zha, S., and Karypis, G · 2022
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Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
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The use of artificial intelligence–based conversational agents (chatbots) for weight loss: scoping review and practical recommendations
Chew, H. S. J · 2022
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Feldman, V., McMillan, A., and Talwar, K · 2022
Cited alongside, same era.
Exploring the limits of differentially private deep learning with group-wise clipping
He, J., Li, X., Yu, D., Zhang, H., Kulkarni, J., Lee, Y. T., Backurs, A., Yu, N., and Bian, J · 2022
Cited alongside, same era.
Illustrating reinforcement learning from human feedback (rlhf)
Lambert, N., Castricato, L., von Werra, L., and Havrilla, A · 2022
Cited alongside, same era.
Large language models can be strong differentially private learners
Li, X., Tramer, F., Liang, P., and Hashimoto, T · 2022
Cited alongside, same era.
Microsoft is bringing chatgpt technology to word, excel and outlook, 2023
CNN · 2023
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Yelp dataset, 2023
Inc, Y · 2023
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Harnessing large-language models to generate private synthetic text
Kurakin, A., Ponomareva, N., Syed, U., MacDermed, L., and Terzis, A · 2023
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Deja vu: Contextual sparsity for efficient llms at inference time
Liu, Z., Wang, J., Dao, T., Zhou, T., Yuan, B., Song, Z., Shrivastava, A., Zhang, C., Tian, Y., Re, C., et al · 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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Fine-tuning language models with just forward passes
Malladi, S., Gao, T., Nichani, E., Damian, A., Lee, J. D., Chen, D., and Arora, S · 2023
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Membership inference attacks against language models via neighbourhood comparison
Mattern, J., Mireshghallah, F., Jin, Z., Schoelkopf, B., Sachan, M., and Berg-Kirkpatrick, T · 2023
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Locally differentially private document generation using zero shot prompting
Utpala, S., Hooker, S., and Chen, P.-Y · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Wang, B., Chen, W., Pei, H., Xie, C., Kang, M., Zhang, C., Xu, C., Xiong, Z., Dutta, R., Schaeffer, R., et al · 2023
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Training private and efficient language models with synthetic data from llms
Yu, D., Backurs, A., Gopi, S., Inan, H., Kulkarni, J., Lin, Z., Xie, C., Zhang, H., and Zhang, W · 2023
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Synthetic text generation with differential privacy: A simple and practical recipe
Yue, X., Inan, H. A., Li, X., Kumar, G., McAnallen, J., Sun, H., Levitan, D., and Sim, R · 2023
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Differentially private synthetic data via foundation model apis 1: Images
Lin, Z., Gopi, S., Kulkarni, J., Nori, H., and Yekhanin, S · 2024
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