3 citations · 3 across the 8 of their papers we have counts for
8 papers
SoftAdaClip: A Smooth Clipping Strategy for Fair and Private Model Training
Dorsa Soleymani, Ali Dadsetan, Frank Rudzicz
Differential privacy (DP) provides strong protection for sensitive data, but often reduces model performance and fairness, especially for underrepresented groups. One major reason…
Re-examining Low Rank adaptation for private LLM fine-tuning
Ali Dadsetan, Frank Rudzicz
Privacy is a central concern when fine-tuning large language models (LLMs) on sensitive data, and differentially private stochastic gradient descent (DP-SGD) -- which clips per-sam…
Trustworthy Medical Question Answering: An Evaluation-Centric Survey
Yinuo Wang, Baiyang Wang, Robert E. Mercer +5
Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence. As large language models (LLMs)…
Can large language models be privacy preserving and fair medical coders?
Ali Dadsetan, Dorsa Soleymani, Xijie Zeng +1
Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy…
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification
Sudipta Singha Roy, Xindi Wang, Robert E. Mercer +1
Long document classification presents challenges in capturing both local and global dependencies due to their extensive content and complex structure. Existing methods often strugg…
The GPT-WritingPrompts Dataset: A Comparative Analysis of Character Portrayal in Short Stories
Xi Yu Huang, Krishnapriya Vishnubhotla, Frank Rudzicz
The improved generative capabilities of large language models have made them a powerful tool for creative writing and storytelling. It is therefore important to quantitatively unde…
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation
Xindi Wang, Robert E. Mercer, Frank Rudzicz
The International Classification of Diseases (ICD) serves as a definitive medical classification system encompassing a wide range of diseases and conditions. The primary objective…
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification
Xindi Wang, Robert E. Mercer, Frank Rudzicz
The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD…