Explanation

Transformers have several key advantages over RNN-based models:

1. Parallel Computation

• RNNs process sequences sequentially (one token at a time), which makes them slow and difficult to parallelize.
• Transformers process all tokens in a sequence simultaneously using self-attention mechanisms, enabling efficient parallel computation on modern hardware (GPUs/TPUs).

2. Better Handling of Long-Term Dependencies

• RNNs suffer from vanishing/exploding gradient problems when processing long sequences, making it difficult to capture long-range dependencies.
• Transformers use self-attention mechanisms that can directly connect any two positions in the sequence, regardless of distance, allowing them to capture long-term dependencies more effectively.

3. Why Other Options Are Incorrect

• Option A: This describes RNNs, not Transformers. RNNs process input sequentially, while Transformers process all tokens in parallel.
• Option B: Transformers don't use convolution filters; they use attention mechanisms. Convolution filters are used in CNNs.
• Option D: Transformers typically have more parameters than RNNs due to their attention mechanisms and multiple layers.

4. Additional Advantages

• Scalability: Transformers scale better with larger datasets and model sizes.
• Global Context: Self-attention provides global context for each token, unlike RNNs which have limited context windows.
• Training Efficiency: Parallel processing makes training faster and more efficient.

This architectural advantage is why Transformers have become the foundation for most state-of-the-art NLP models like BERT, GPT, and T5.