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📖Reading•15 min

The Limitations of RNNs

Understand why transformers replaced RNNs for most NLP tasks.

Why Transformers Beat RNNs

RNN Limitations

1. Sequential Processing

  • Can't parallelize
  • Slow training on long sequences

    • 2. Limited Long-Range Dependencies

  • Information fades over distance
  • Even LSTMs struggle with very long contexts

    • 3. Fixed Capacity

  • Same hidden size regardless of sequence length

    • Enter the Transformer

    Key Innovations:

  • Parallel processing: All positions at once
  • Attention: Direct connections to any position
  • Scalability: Grows with compute

    • Performance Comparison

    Task | LSTM | Transformer |
    |------|------|-------------|
    Machine Translation | Good | Excellent |
    Long Documents | Poor | Good |
    Training Speed | Slow | Fast (parallel) |
    Scale | Limited | Scales well |

    The 2017 paper "Attention Is All You Need" changed everything.

    🎯 Key Takeaways

    • ✓RNNs process sequentially, limiting parallelization
    • ✓Long-range dependencies are hard for RNNs
    • ✓Transformers enable parallel processing
    • ✓Attention allows direct access to any position

    📚 Additional Resources

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    Sequence Models (RNN, LSTM)
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    Attention Is All You Need