What are the main challenges faced by transformer models?

What are the main challenges faced by transformer models?

Transformer models, while powerful, face challenges like high computational costs, data inefficiency, and difficulty in handling long sequences.

Can you explain why high computational costs are a challenge?

High computational costs arise from the large number of parameters in transformers, requiring significant resources for training and inference.

What about data inefficiency?

Data inefficiency occurs because transformers often need vast amounts of data to generalize well, which can be a barrier in low-data scenarios.

How do transformers handle long sequences?

Transformers struggle with long sequences due to their quadratic complexity in attention mechanisms, leading to memory and processing issues.

Are there any solutions to these challenges?

Yes, researchers are exploring techniques like sparse attention, model distillation, and more efficient architectures to mitigate these challenges.