What are the limitations of transformers?

What are the limitations of transformers in natural language processing?

Transformers have several limitations, including high computational costs, difficulty in handling long sequences, and a tendency to require large amounts of data for training.

Can you explain why they struggle with long sequences?

Sure! Transformers use self-attention mechanisms, which can become inefficient as the sequence length increases, leading to higher memory and processing requirements.

Are there any specific tasks where transformers perform poorly?

Yes, transformers can struggle with tasks that require deep contextual understanding or reasoning over long documents, such as certain types of question answering.

What alternatives exist to transformers for NLP?

Alternatives include recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recent architectures like RNN-Transformer hybrids.

How can researchers address these limitations?

Researchers are exploring techniques like sparse attention, model distillation, and hybrid architectures to mitigate these limitations.