2 citations · 3 across the 5 of their papers we have counts for
5 papers
Limits for Learning with Language Models
Nicholas Asher, Swarnadeep Bhar, Akshay Chaturvedi +2
With the advent of large language models (LLMs), the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks. The l…
Are fairness metric scores enough to assess discrimination biases in machine learning?
Fanny Jourdan, Laurent Risser, Jean-Michel Loubes +1
This paper presents novel experiments shedding light on the shortcomings of current metrics for assessing biases of gender discrimination made by machine learning algorithms on tex…
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks
Fanny Jourdan, Agustin Picard, Thomas Fel +3
Transformer architectures are complex and their use in NLP, while it has engendered many successes, makes their interpretability or explainability challenging. Recent debates have…
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Fanny Jourdan, Titon Tshiongo Kaninku, Nicholas Asher +2
Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications whic…
Interpretive Blindness
Nicholas Asher, Julie Hunter
We model here an epistemic bias we call \textit{interpretive blindness} (IB). IB is a special problem for learning from testimony, in which one acquires information only from text…