What are the main challenges faced by transformer models?

What are the main challenges faced by transformer models?

Transformer models face several challenges, including 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 self-attention mechanism, which scales quadratically with the input sequence length, making it resource-intensive.

What about data inefficiency?

Data inefficiency occurs because transformers require large amounts of labeled data to perform well, which can be hard to obtain.

How do transformers handle long sequences?

Transformers struggle with long sequences due to their fixed context window, leading to potential loss of information in longer texts.

Are there any solutions to these challenges?

Yes, researchers are exploring techniques like sparse attention, model distillation, and better pre-training methods to address these issues.