11 citations · 47 across the 13 of their papers we have counts for
14 papers
A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation
Jarad Forristal, Niloofar Mireshghallah, Greg Durrett +1
Recent work has shown that energy-based language modeling is an effective framework for controllable text generation because it enables flexible integration of arbitrary discrimina…
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
Mengke Zhang, Tianxing He, Tianle Wang +5
In the current user-server interaction paradigm of prompted generation with large language models (LLM) on cloud, the server fully controls the generation process, which leaves zer…
Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization
Aman Priyanshu, Supriti Vijay, Ayush Kumar +2
LLM-powered chatbots are becoming widely adopted in applications such as healthcare, personal assistants, industry hiring decisions, etc. In many of these cases, chatbots are fed s…
Membership Inference Attacks against Language Models via Neighbourhood Comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin +3
Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not, and are widely used for assessing the…
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
Niloofar Mireshghallah, Justus Mattern, Sicun Gao +2
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machi…
Privacy-Preserving Domain Adaptation of Semantic Parsers
Fatemehsadat Mireshghallah, Yu Su, Tatsunori Hashimoto +2
Task-oriented dialogue systems often assist users with personal or confidential matters. For this reason, the developers of such a system are generally prohibited from observing ac…
Non-Parametric Temporal Adaptation for Social Media Topic Classification
Fatemehsadat Mireshghallah, Nikolai Vogler, Junxian He +3
User-generated social media data is constantly changing as new trends influence online discussion and personal information is deleted due to privacy concerns. However, most current…
Memorization in NLP Fine-tuning Methods
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang +2
Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little…
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models
Fatemehsadat Mireshghallah, Kartik Goyal, Taylor Berg-Kirkpatrick
Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute d…
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal +2
The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy v…
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi +3
Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these model…
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness
Fatemehsadat Mireshghallah, Taylor Berg-Kirkpatrick
Text style can reveal sensitive attributes of the author (e.g. race or age) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic dec…
Efficient Hyperparameter Optimization for Differentially Private Deep Learning
Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah +1
Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge. Unlike the typical SGD, private datasets cannot be used man…
Benchmarking Differential Privacy and Federated Learning for BERT Models
Priyam Basu, Tiasa Singha Roy, Rakshit Naidu +3
Natural Language Processing (NLP) techniques can be applied to help with the diagnosis of medical conditions such as depression, using a collection of a person's utterances. Depres…