26 citations · 47 across the 8 of their papers we have counts for
8 papers
What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages
Viktor Mihaylov, Aleksandar Shtedritski
This paper investigates biases of Large Language Models (LLMs) through the lens of grammatical gender. Drawing inspiration from seminal works in psycholinguistics, particularly the…
HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits
Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie +3
Benchmarks have been essential for driving progress in machine learning. A better understanding of LLM capabilities on real world tasks is vital for safe development. Designing ade…
VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
Siobhan Mackenzie Hall, Fernanda Gonçalves Abrantes, Hanwen Zhu +3
We introduce VisoGender, a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, i…
Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets
Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall +3
Vision-language models are growing in popularity and public visibility to generate, edit, and caption images at scale; but their outputs can perpetuate and amplify societal biases…
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat +4
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness…
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
Hannah Rose Kirk, Yennie Jun, Paulius Rauba +7
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released…
Privacy-preserving Object Detection
Peiyang He, Charlie Griffin, Krzysztof Kacprzyk +4
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to p…
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models
Hannah Kirk, Yennie Jun, Haider Iqbal +5
The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these…