{"list":"other","total":51,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"1509.01626","title":"Character-level Convolutional Networks for Text Classification","created":"2015-09-04","cited_by_count":2330},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2202.12837","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","created":"2022-02-25","cited_by_count":1434},{"id":"1702.07476","title":"Renyi Differential Privacy","created":"2017-02-24","cited_by_count":650},{"id":"2306.11698","title":"DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models","created":"2023-06-20","cited_by_count":518},{"id":"2304.11015","title":"DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction","created":"2023-04-21","cited_by_count":477},{"id":"2110.05679","title":"Large Language Models Can Be Strong Differentially Private Learners","created":"2021-10-12","cited_by_count":412},{"id":"2110.06500","title":"Differentially Private Fine-tuning of Language Models","created":"2021-10-13","cited_by_count":400},{"id":"1610.05755","title":"Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data","created":"2016-10-18","cited_by_count":348},{"id":"1802.08908","title":"Scalable Private Learning with PATE","created":"2018-02-24","cited_by_count":269},{"id":"1808.00087","title":"Subsampled R\\'enyi Differential Privacy and Analytical Moments Accountant","created":"2018-07-31","cited_by_count":236},{"id":"1910.08902","title":"Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations","created":"2019-10-20","cited_by_count":119},{"id":"2002.02925","title":"BERT-of-Theseus: Compressing BERT by Progressive Module Replacing","created":"2020-02-07","cited_by_count":113},{"id":"2210.14348","title":"Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe","created":"2022-10-25","cited_by_count":108},{"id":"2309.11765","title":"Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation","created":"2023-09-21","cited_by_count":96},{"id":"1905.09982","title":"Hypothesis Testing Interpretations and Renyi Differential Privacy","created":"2019-05-24","cited_by_count":85},{"id":"2305.15594","title":"Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models","created":"2023-05-24","cited_by_count":83},{"id":"2210.13918","title":"Differentially Private Language Models for Secure Data Sharing","created":"2022-10-25","cited_by_count":68},{"id":"2111.06578","title":"Differential privacy and robust statistics in high dimensions","created":"2021-11-12","cited_by_count":64},{"id":"2310.16111","title":"Locally Differentially Private Document Generation Using Zero Shot Prompting","created":"2023-10-24","cited_by_count":63},{"id":"1803.02596","title":"Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain","created":"2018-03-07","cited_by_count":61},{"id":"2312.03724","title":"DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer","created":"2023-11-27","cited_by_count":57},{"id":"1906.02830","title":"Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation","created":"2019-06-06","cited_by_count":52},{"id":"2207.01193","title":"A Customized Text Sanitization Mechanism with Differential Privacy","created":"2022-07-04","cited_by_count":52},{"id":"2212.01539","title":"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping","created":"2022-12-03","cited_by_count":52},{"id":"2305.01639","title":"Privacy-Preserving In-Context Learning for Large Language Models","created":"2023-05-02","cited_by_count":50},{"id":"2401.04343","title":"Private Fine-tuning of Large Language Models with Zeroth-order Optimization","created":"2024-01-09","cited_by_count":41},{"id":"2402.13659","title":"Privacy-Preserving Instructions for Aligning Large Language Models","created":"2024-02-21","cited_by_count":35},{"id":"2008.08007","title":"Differentially Private Clustering: Tight Approximation Ratios","created":"2020-08-18","cited_by_count":34},{"id":"2205.13621","title":"Differentially Private Decoding in Large Language Models","created":"2022-05-26","cited_by_count":34},{"id":"2201.00971","title":"Submix: Practical Private Prediction for Large-Scale Language Models","created":"2022-01-04","cited_by_count":31},{"id":"1604.05590","title":"Locating a Small Cluster Privately","created":"2016-04-19","cited_by_count":20},{"id":"1707.04766","title":"Clustering Algorithms for the Centralized and Local Models","created":"2017-07-15","cited_by_count":18},{"id":"2305.15008","title":"Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization","created":"2023-05-24","cited_by_count":17},{"id":"2404.04360","title":"Prompt Public Large Language Models to Synthesize Data for Private On-device Applications","created":"2024-04-05","cited_by_count":17},{"id":"1606.03572","title":"Differentially Private Random Decision Forests using Smooth Sensitivity","created":"2016-06-11","cited_by_count":16},{"id":"2306.01471","title":"Guiding Text-to-Text Privatization by Syntax","created":"2023-06-02","cited_by_count":14},{"id":"2306.01457","title":"Driving Context into Text-to-Text Privatization","created":"2023-06-02","cited_by_count":13},{"id":"2212.10520","title":"Privacy-Preserving Domain Adaptation of Semantic Parsers","created":"2022-12-20","cited_by_count":12},{"id":"2403.05681","title":"DP-TabICL: In-Context Learning with Differentially Private Tabular Data","created":"2024-03-08","cited_by_count":11},{"id":"2305.03573","title":"In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models","created":"2023-05-05","cited_by_count":10},{"id":"2203.16100","title":"Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE","created":"2022-03-30","cited_by_count":9},{"id":"2211.02139","title":"Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity","created":"2022-11-03","cited_by_count":8},{"id":"2301.00301","title":"Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy","created":"2022-12-31","cited_by_count":6},{"id":"2403.04124","title":"Privacy-preserving Fine-tuning of Large Language Models through Flatness","created":"2024-03-07","cited_by_count":6},{"id":"1805.10341","title":"An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm","created":"2018-05-25","cited_by_count":4},{"id":"2003.00505","title":"Differentially Private Deep Learning with Smooth Sensitivity","created":"2020-03-01","cited_by_count":4},{"id":"2209.07716","title":"Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning","created":"2022-09-16","cited_by_count":4},{"id":"2003.13955","title":"Differentially Private Naive Bayes Classifier using Smooth Sensitivity","created":"2020-03-31","cited_by_count":2}],"next_cursor":null}