11 citations · 40 across the 11 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…