2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Why Don't Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed, Goncalo Mordido, Ioana Baldini +1
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics a…
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf +16
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In co…
SocialStigmaQA: A Benchmark to Uncover Stigma Amplification in Generative Language Models
Manish Nagireddy, Lamogha Chiazor, Moninder Singh +1
Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchm…
Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian +2
The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the la…
Keeping Up with the Language Models: Systematic Benchmark Extension for Bias Auditing
Ioana Baldini, Chhavi Yadav, Manish Nagireddy +2
Bias auditing of language models (LMs) has received considerable attention as LMs are becoming widespread. As such, several benchmarks for bias auditing have been proposed. At the…