Sharif Digital Repository

Recent advancements in deep learning methods have led to significant progress in Language Models. However, training these models on vast amounts of real-world and internet data has resulted in gender bias. Given the increasing application of these models, identifying and mitigating this bias is of particular importance. Previous efforts to address this issue often required extensive datasets, long training times, and heavy hardware resources, which also led to the forgetting of the model’s prior knowledge. Furthermore, existing evaluation metrics only assessed bias across the entire dataset and did not consider different topics separately. Therefore, the dependency of these metrics on...