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Found 5 papers, 2 papers with code
Fairness-Aware Structured Pruning in Transformers
The increasing size of large language models (LLMs) has introduced challenges in their training and inference.
Should We Attend More or Less? Modulating Attention for Fairness
In this work, we investigate the role of attention, a widely-used technique in current state-of-the-art NLP models, in the propagation of social biases.
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset.
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation
Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed.
microGAN: Promoting Variety through Microbatch Discrimination
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.
