4 papers
SynBench: A Benchmark for Differentially Private Text Generation
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +9
Synthetic text generation with Differential Privacy (DP) guarantees emerges as a principled approach that can enable the sharing of sensitive datasets across institutional and regu…
Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms
Yuping Wu, Viktor Schlegel, Warren Del-Pinto +10
Training data is fundamental to the success of modern machine learning models, yet in high-stakes domains such as healthcare, the use of real-world training data is severely constr…
Evaluating Differentially Private Generation of Domain-Specific Text
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +7
Generative AI offers transformative potential for high-stakes domains such as healthcare and finance, yet privacy and regulatory barriers hinder the use of real-world data. To addr…
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Aishik Nagar, Viktor Schlegel, Thanh-Tung Nguyen +4
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document su…