Niloofar Mireshghallah

According to our database, Niloofar Mireshghallah authored at least 52 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Differentially Private Learning Needs Better Model Initialization and Self-Distillation.

Ivoline C. Ngong, Joseph P. Near, Niloofar Mireshghallah

CoRR, 2024

AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text.

Ximing Lu, Melanie Sclar, Skyler Hallinan, Niloofar Mireshghallah, Jiacheng Liu, Seungju Han, Allyson Ettinger, Liwei Jiang, Khyathi Raghavi Chandu, Nouha Dziri, Yejin Choi

CoRR, 2024

HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions.

Xuhui Zhou, Hyunwoo Kim, Faeze Brahman, Liwei Jiang, Hao Zhu, Ximing Lu, Frank Xu, Bill Yuchen Lin, Yejin Choi, Niloofar Mireshghallah, Ronan Le Bras, Maarten Sap

CoRR, 2024

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild.

Niloofar Mireshghallah, Maria Antoniak, Yash More, Yejin Choi, Golnoosh Farnadi

CoRR, 2024

WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models.

Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, Nouha Dziri

CoRR, 2024

Breaking News: Case Studies of Generative AI's Use in Journalism.

Natalie Grace Brigham, Chongjiu Gao, Tadayoshi Kohno, Franziska Roesner, Niloofar Mireshghallah

CoRR, 2024

Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs.

Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah, Hyunwoo Kim, Yulia Tsvetkov, Yejin Choi, Sherif Saad, Santu Rana

CoRR, 2024

Do Membership Inference Attacks Work on Large Language Models?

Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, Hannaneh Hajishirzi

CoRR, 2024

A Roadmap to Pluralistic Alignment.

Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L. Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi

CoRR, 2024

LatticeGen: Hiding Generated Text in a Lattice for Privacy-Aware Large Language Model Generation on Cloud.

Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Niloofar Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Position: A Roadmap to Pluralistic Alignment.

Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L. Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation.

Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory.

Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi

Proceedings of the Twelfth International Conference on Learning Representations, 2024

CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation.

Tong Chen, Akari Asai, Niloofar Mireshghallah, Sewon Min, James Grimmelmann, Yejin Choi, Hannaneh Hajishirzi, Luke Zettlemoyer, Pang Wei Koh

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors.

Niloofar Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, Taylor Berg-Kirkpatrick

Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

2023

Auditing and Mitigating Safety Risks in Large Language Models

Niloofar Mireshghallah

PhD thesis, 2023

Report of the 1st Workshop on Generative AI and Law.

A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide

CoRR, 2023

Misusing Tools in Large Language Models With Visual Adversarial Examples.

Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Earlence Fernandes

CoRR, 2023

LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud.

Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Fatemehsadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov

CoRR, 2023

Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization.

Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, Fatemehsadat Mireshghallah

CoRR, 2023

Smaller Language Models are Better Black-box Machine-Generated Text Detectors.

Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, Taylor Berg-Kirkpatrick

CoRR, 2023

Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN.

Niloofar Mireshghallah, Nikolai Vogler, Junxian He, Omar Florez, Ahmed El-Kishky, Taylor Berg-Kirkpatrick

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation.

Jarad Forristal, Fatemehsadat Mireshghallah, Greg Durrett, Taylor Berg-Kirkpatrick

Proceedings of the 27th Conference on Computational Natural Language Learning, 2023

Privacy-Preserving Domain Adaptation of Semantic Parsers.

Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto, Jason Eisner, Richard Shin

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Membership Inference Attacks against Language Models via Neighbourhood Comparison.

Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Non-Parametric Temporal Adaptation for Social Media Topic Classification.

Fatemehsadat Mireshghallah, Nikolai Vogler, Junxian He, Omar Florez, Ahmed El-Kishky, Taylor Berg-Kirkpatrick

CoRR, 2022

Memorization in NLP Fine-tuning Methods.

Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, Taylor Berg-Kirkpatrick

CoRR, 2022

Mix and Match: Learning-free Controllable Text Generation using Energy Language Models.

Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick

CoRR, 2022

Differentially Private Model Compression.

Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis.

Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

What Does it Mean for a Language Model to Preserve Privacy?

Hannah Brown, Katherine Lee, Fatemehsadat Mireshghallah, Reza Shokri, Florian Tramèr

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

An Empirical Analysis of Memorization in Fine-tuned Autoregressive Language Models.

Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David E. Evans, Taylor Berg-Kirkpatrick

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks.

Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, Taylor Berg-Kirkpatrick, Reza Shokri

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models.

Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Efficient Hyperparameter Optimization for Differentially Private Deep Learning.

Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah, Mohammad Malekzadeh

CoRR, 2021

Benchmarking Differential Privacy and Federated Learning for BERT Models.

Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zümrüt Müftüoglu, Sahib Singh, Fatemehsadat Mireshghallah

CoRR, 2021

When Differential Privacy Meets Interpretability: A Case Study.

Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, Fatemehsadat Mireshghallah

CoRR, 2021

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask

CoRR, 2021

Privacy Regularization: Joint Privacy-Utility Optimization in Language Models.

Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim

CoRR, 2021

Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy.

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh

Proceedings of the WWW '21: The Web Conference 2021, 2021

Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels.

Fatemehsadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

U-Noise: Learnable Noise Masks for Interpretable Image Segmentation.

Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, Georgios Kaissis

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness.

Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks.

Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh

IEEE Micro, 2020

Privacy in Deep Learning: A Survey.

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh

CoRR, 2020

A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference.

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean M. Tullsen, Hadi Esmaeilzadeh

CoRR, 2020

Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization.

Ahmed T. Elthakeb, Prannoy Pilligundla, Fatemehsadat Mireshghallah, Tarek Elgindi, Charles-Alban Deledalle, Hadi Esmaeilzadeh

CoRR, 2020

Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks.

Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Alexander Cloninger, Hadi Esmaeilzadeh

Proceedings of the 37th International Conference on Machine Learning, 2020

Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy.

Tom Farrand, Fatemehsadat Mireshghallah, Sahib Singh, Andrew Trask

Proceedings of the PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020

Shredder: Learning Noise Distributions to Protect Inference Privacy.

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Ali Jalali, Dean M. Tullsen, Hadi Esmaeilzadeh

Proceedings of the ASPLOS '20: Architectural Support for Programming Languages and Operating Systems, 2020

2019

Energy-Efficient Permanent Fault Tolerance in Hard Real-Time Systems.

Fatemehsadat Mireshghallah, Mohammad Bakhshalipour, Mohammad Sadrosadati, Hamid Sarbazi-Azad

IEEE Trans. Computers, 2019

Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge.

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean M. Tullsen, Hadi Esmaeilzadeh

CoRR, 2019