- RNNs Vs LSTMs Vs GRUs Vs Transformers

πŸ” PART 1: Comparing RNN, LSTM, GRU, and Transformer Architectures for Language Modeling

Feature RNN LSTM GRU Transformer
Architecture Type Sequential Sequential with memory Sequential with gates Attention-based, fully parallel
Sequence Handling Step-by-step Step-by-step Step-by-step Entire sequence in parallel
Memory Short-term Long-term memory Long-term (simplified) Global attention
Vanishing Gradient Severe Mitigated Mitigated Avoided via residuals + attention
Parallelization ❌ No ❌ No ❌ No βœ… Yes
Long-range Dependencies Poor Moderate Moderate Excellent
Training Speed Slow Slower Faster than LSTM Fast (GPU/TPU optimized)
Scalability Poor Moderate Moderate Excellent
Parameter Sharing Yes Yes Yes Yes (though often huge models)
Applications Basic NLP tasks MT, Speech, Captioning Chatbots, TTS All SOTA NLP tasks

πŸ” PART 2: Why Transformers Outperform RNN-based Models

πŸ”„ 1. Parallelization vs Sequential Bottleneck

  • RNN/LSTM/GRU: Process input sequentially (token by token), preventing parallelization.
  • Transformer: Uses self-attention over all tokens β†’ processes whole sequence in parallel.

βœ… Result: Transformers are faster and utilize GPU/TPU acceleration better.

🧠 2. Modeling Long-Range Dependencies

  • RNNs forget earlier context due to vanishing gradients.
  • LSTM/GRU improve this with gating mechanisms.
  • Transformer attention enables direct access to all tokens.

βœ… Result: Transformers model global context more effectively.

πŸ“š 3. Richer Representations via Attention

  • Attention lets models focus on important words, regardless of position.
  • Example: β€œThe dog, which was barking loudly, ran away.” β†’ β€œran” aligns with β€œdog”.

βœ… Result: Better handling of syntactic and semantic dependencies.

πŸš€ 4. Scalability & Transfer Learning

  • Transformers (e.g., GPT, BERT) scale with data/params.
  • Pretraining β†’ Fine-tuning paradigm dominates modern NLP.

βœ… Result: One pretrained Transformer can be reused for many downstream tasks.

🧩 5. Modularity and Customization

  • Transformers are modular: multi-head attention, rotary encoding, sparse layers, retrieval augmentation, etc.

βœ… Result: Easier experimentation and SOTA innovations.