5 citations · 15 across the 16 of their papers we have counts for
6 papers · 1 filter
FlexModel: A Framework for Interpretability of Distributed Large Language Models
Matthew Choi, Muhammad Adil Asif, John Willes +1
With the growth of large language models, now incorporating billions of parameters, the hardware prerequisites for their training and deployment have seen a corresponding increase.…
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets
Fatemeh Tavakoli, D. B. Emerson, Sana Ayromlou +5
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning…
On The Role of Reasoning in the Identification of Subtle Stereotypes in Natural Language
Jacob-Junqi Tian, Omkar Dige, D. B. Emerson +1
Large language models (LLMs) are trained on vast, uncurated datasets that contain various forms of biases and language reinforcing harmful stereotypes that may be subsequently inhe…
Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?
Omkar Dige, Jacob-Junqi Tian, David Emerson +1
As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the lear…
Soft-prompt Tuning for Large Language Models to Evaluate Bias
Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab +3
Prompting large language models has gained immense popularity in recent years due to the advantage of producing good results even without the need for labelled data. However, this…
An A Posteriori Error Estimator for Electrically Coupled Liquid Crystal Equilibrium Configurations
J. H. Adler, D. B. Emerson
This paper derives an a posteriori error estimator for the nonlinear first-order optimality conditions associated with the electrically and flexoelectrically coupled Frank-Oseen mo…