What are the limitations of transformer models?

What are the limitations of transformer models?

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

Can you explain why they are data inefficient?

Sure! Transformer models often require large amounts of data to train effectively, and they can struggle with generalizing from smaller datasets.

What about their performance on long sequences?

Transformers can face challenges with long sequences due to their quadratic complexity in attention mechanisms, which can lead to memory issues.

Are there any alternatives to transformers?

Yes, alternatives like RNNs and CNNs exist, but they come with their own sets of limitations. Researchers are also exploring hybrid models.

How do transformers compare to RNNs?

Transformers generally outperform RNNs in terms of parallelization and handling long-range dependencies, but RNNs can be more efficient for smaller datasets.