What are the limitations of transformer models in AI?

What are the limitations of transformer models in AI?

Transformer models have several limitations, including high computational costs, data inefficiency, and difficulty in handling long sequences.

Can you explain why they are computationally expensive?

Yes, transformers require significant resources due to their self-attention mechanism, which scales quadratically with the input size.

What about their performance on small datasets?

Transformers tend to overfit on small datasets because they have a large number of parameters, making them less effective without sufficient data.

Are there any alternatives to transformers?

Yes, alternatives include recurrent neural networks (RNNs) and convolutional neural networks (CNNs), though they have their own limitations.

How do transformers compare to RNNs in terms of sequence processing?

Transformers process sequences in parallel, making them faster and more efficient than RNNs, which process sequentially.