Distilled BERT

Distilled BERT refers to techniques that compress the large, computationally expensive BERT model into smaller, faster versions while preserving much of its performance. Current research focuses on improving distillation methods, exploring different student model architectures (like LSTMs and smaller Transformers), and optimizing for various computational constraints through techniques such as quantization and length adaptation. This work is significant because it enables the deployment of BERT-like capabilities on resource-limited devices and accelerates inference times for various natural language processing tasks, broadening the accessibility and applicability of these powerful models.