What are the limits of transformer models?

What are the main limitations of transformer models?

Transformer models, while powerful, have limitations such as high computational costs, data requirements, and difficulty with long-term dependencies.

Can you explain why they struggle with long-term dependencies?

Transformers use self-attention mechanisms, which can lose context over long sequences, making it hard to maintain coherence.

Are there any specific tasks where transformers perform poorly?

Yes, tasks requiring deep reasoning or understanding of complex relationships, like certain mathematical problems, can be challenging.

What are some alternatives to transformer models?

Alternatives include recurrent neural networks (RNNs), convolutional neural networks (CNNs), and hybrid models that combine different architectures.