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 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 aforementioned seminal work of (CITATION) and (CITATION) and propose DeepSoftDebias, an algorithm that uses a neural network to perform ‘soft debiasing’. We exhaustively evaluate this algorithm across a variety of state-of-the-art datasets, accuracy metrics, and challenging NLP tasks. On a wide range of metrics, we find that DeepSoftDebias outperforms the current state-of-the-art methods at reducing bias across gender, race, and religion.