18 citations · 19 across the 5 of their papers we have counts for
5 papers
SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models
Hoda Fakharzadehjahromy, Emil Wiman, Andreas Bueff +2
Language models for low-resource languages still make frequent grammatical errors. Alignment methods such as RLHF and DPO can correct them. However, they require human judgements,…
Fair4Free: Generating High-fidelity Fair Synthetic Samples using Data Free Distillation
Md Fahim Sikder, Daniel de Leng, Fredrik Heintz
This work presents Fair4Free, a novel generative model to generate synthetic fair data using data-free distillation in the latent space. Fair4Free can work on the situation when th…
Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng +1
Data Fairness is a crucial topic due to the recent wide usage of AI powered applications. Most of the real-world data is filled with human or machine biases and when those data are…
FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability
Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng +1
We present FairX, an open-source Python-based benchmarking tool designed for the comprehensive analysis of models under the umbrella of fairness, utility, and eXplainability (XAI).…
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
Resmi Ramachandranpillai, Md Fahim Sikder, David Bergström +1
Synthetic data generation offers a promising solution to enhance the usefulness of Electronic Healthcare Records (EHR) by generating realistic de-identified data. However, the exis…