2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
Paying Alignment Tax with Contrastive Learning
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +1
Current debiasing approaches often result a degradation in model capabilities such as factual accuracy and knowledge retention. Through systematic evaluation across multiple benchm…
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
Buse Sibel Korkmaz, Rahul Nair, Elizabeth M. Daly +3
Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it t…
Black-box Uncertainty Quantification Method for LLM-as-a-Judge
Nico Wagner, Michael Desmond, Rahul Nair +6
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty…
On Efficient and Statistical Quality Estimation for Data Annotation
Jan-Christoph Klie, Juan Haladjian, Marc Kirchner +1
Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are o…