Transformer Ensemble

Transformer ensembles combine the predictions of multiple transformer-based models to improve performance on various natural language processing and computer vision tasks. Current research focuses on applying this technique to challenging problems like hate speech detection, misinformation identification, and medical image analysis, often leveraging pre-trained models like BERT and RoBERTa and exploring ensemble methods such as voting and knowledge distillation. This approach demonstrates significant improvements in accuracy and robustness compared to single-model approaches, impacting fields ranging from online safety to healthcare diagnostics.