RNN vs Transformer
RNN (Recurrent Neural Network) — Word-by-Word Processing
RNNs process text sequentially, one word at a time. At each step, the model reads the current word and updates a hidden memory state that carries information from all previous words. This means each new word depends on the previous words through that hidden state. :contentReference[oaicite:0]{index=0}
RNNs are inherently sequential — the next step cannot start until the previous one finishes. :contentReference[oaicite:1]{index=1}
Analogy: Imagine an assembly line where each worker only knows what the previous worker passed on — nothing else.
Transformers — All Words at Once Using Self-Attention
Instead of processing words one by one, Transformers look at the entire sentence simultaneously using a mechanism called self-attention. :contentReference[oaicite:2]{index=2}
For each word, the Transformer computes how much attention it should pay to every other word in the sentence and uses that to build contextual meaning. Because of this, Transformers can process all tokens in parallel. :contentReference[oaicite:3]{index=3}
Analogy: Instead of an assembly line, it’s like everyone discussing at a round table — every word can “talk” to every other word instantly.
RNN vs. Transformer — Quick Comparison
| Feature | RNN | Transformer |
|---|---|---|
| Processing | One word at a time (sequential) | All words simultaneously (parallel) |
| Context | Hidden state carries context step by step | Self-attention captures context globally |
| Speed | Slower due to sequence dependency | Faster due to parallelization |
| Long-range understanding | Harder (information fades) | Stronger (direct connections) |
Summary
RNNs scan text like a reader moving through a book — one page at a time — carrying a memory state. Transformers instead scan the entire book at once and build a web of relationships between all words using self-attention, allowing faster and richer context understanding.