From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings
Abstract
Embeddings play a pivotal role in the efficacy of Large Language Models. They are the bedrock on which these models grasp contextual relationships and foster a more nuanced understanding of language and consequently perform remarkably on a plethora of complex tasks that require a fundamental understanding of human language. Given that these embeddings themselves often reflect or exhibit bias, it stands to reason that these models may also inadvertently learn this bias. In this work, we build on the seminal previous work and propose DeepSoftDebias, an algorithm that uses a neural network to perform 'soft debiasing'. We exhaustively evaluate this algorithm across a variety of SOTA datasets, accuracy metrics, and challenging NLP tasks. We find that DeepSoftDebias outperforms the current state-of-the-art methods at reducing bias across gender, race, and religion.
Cite
@article{arxiv.2402.11512,
title = {From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings},
author = {Aishik Rakshit and Smriti Singh and Shuvam Keshari and Arijit Ghosh Chowdhury and Vinija Jain and Aman Chadha},
journal= {arXiv preprint arXiv:2402.11512},
year = {2025}
}
Comments
Accepted at COLING 2025