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 inherently slow for training.
• Transformers process all tokens in a sequence simultaneously through self-attention mechanisms, enabling parallel computation and significantly faster training times.

2. Better Handling of Long-Term Dependencies

• RNNs suffer from vanishing/exploding gradient problems when dealing with 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. Architectural Differences

• Option A is incorrect: Transformers do NOT process input sequentially - this is actually a characteristic of RNNs.
• Option B is incorrect: Transformers do not rely on convolution filters; they use attention mechanisms.
• Option D is incorrect: Transformers typically have MORE parameters than RNNs due to their attention mechanisms and feed-forward networks.

Key Transformer Features:

• Self-Attention: Allows the model to weigh the importance of different words in a sequence relative to each other
• Positional Encoding: Injects information about word order since Transformers don't process sequentially
• Multi-Head Attention: Enables the model to focus on different parts of the sequence simultaneously

This parallel processing capability and superior handling of long-range dependencies make Transformers particularly well-suited for large-scale language modeling tasks.