126 citations · 613 across the 33 of their papers we have counts for
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Private Synthetic Text Generation with Diffusion Models
Sebastian Ochs, Ivan Habernal
How capable are diffusion models of generating synthetics texts? Recent research shows their strengths, with performance reaching that of auto-regressive LLMs. But are they also go…
Private Language Models via Truncated Laplacian Mechanism
Tianhao Huang, Tao Yang, Ivan Habernal +2
Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent privacy leakage, researchers have investigated word-level perturbations, relying on the form…
The Impact of Inference Acceleration on Bias of LLMs
Elisabeth Kirsten, Ivan Habernal, Vedant Nanda +1
Last few years have seen unprecedented advances in capabilities of Large Language Models (LLMs). These advancements promise to benefit a vast array of application domains. However,…
Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš +7
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.…
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation
Doan Nam Long Vu, Timour Igamberdiev, Ivan Habernal
Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice o…