bibtext: @inproceedings{
  zayed2024should,
  title={Should We Attend More or Less? Modulating Attention for Fairness},
  author={Abdelrahman Zayed and Goncalo Mordido and Samira Shabanian and Sarath Chandar},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=CI7D2kiih1}
  }
abstract: The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of tasks, it also poses the risk of models learning harmful biases from the data, such as gender stereotypes. 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. Specifically, we study the relationship between the entropy of the attention distribution and the model's performance and fairness. We then propose a novel method for modulating attention weights to improve model fairness after training. Since our method is only applied post-training and pre-inference, it is an intra-processing method and is, therefore, less computationally expensive than existing in-processing and pre-processing approaches. Our results show an increase in fairness and minimal performance loss on different text classification and generation tasks using language models of varying sizes.
title: Should We Attend More or Less? Modulating Attention for Fairness
authors: Abdelrahman Zayed Goncalo Mordido Samira Shabanian Sarath Chandar