Research Background
Before 2017, Natural Language Processing (NLP) was dominated by Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks. These models processed data sequentially—word by word. This meant:
1. Slow training: You couldn't parallelize the process across thousands of GPUs.
2. Forgetting: They struggled to remember context from the beginning of a long sentence by the time they reached the end.
The paper "Attention Is All You Need" (Vaswani et al.) proposed discarding recurrence entirely in favor of an architecture based solely on attention mechanisms.
Core Technical Explanation
The Transformer is an Encoder-Decoder architecture (though GPT uses only the Decoder). Its secret sauce is Self-Attention.
Scaled Dot-Product Attention
At its core, the model calculates how much every word in a sentence should "attend" to every other word.
The formula is:
Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V
- Queries (Q): What I am looking for?
- Keys (K): What descriptor do I have?
- Values (V): What content do I contain?
If a Query matches a Key, the model pays attention to the Value. For example, in "The bank of the river," the word "bank" needs to attend to "river" to know it's not a financial institution. The dot product determines this relevance score.
Multi-Head Attention
Instead of doing this once, the Transformer does it multiple times in parallel ("heads"). One head might focus on grammar, another on semantic relationship, another on coreference.
What the Data Shows
The shift to Transformers led to log-scale improvements in performance (Perplexity scores).
| Architecture | Training Efficiency | Long-Range Dependency | Translation BLEU Score |
|---|---|---|---|
| LSTM (Prev SOTA) | Low (Sequential) | Weak | 26.0 (En-De) |
| Transformer (Base) | High (Parallel) | Strong | 27.3 (En-De) |
| Transformer (Big) | Very High | Strong | 28.4 (En-De) |
Limitations & Open Problems
1. Quadratic Complexity O(N^2): The attention mechanism compares every token to every other token. Doubling the context length quadruples the compute cost. This makes infinite context windows extremely expensive.
2. The Hallucination Feature: Because the model is probabilistic (predicting the next token based on statistical likelihood), it can confidently state falsehoods if they "sound" statistically probable.
Why This Matters
Transformers didn't just improve translation; they created Generic Intelligence. The same architecture used for predicting text is now used for folding proteins (AlphaFold), generating images (Sora), and controlling robots. It is the "steam engine" of the 21st century.
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Verified by Global AI News Editorial Board. Sources: Vaswani et al. (2017), "Attention Is All You Need"; Jay Alammar, "The Illustrated Transformer"