Introduction
Since its introduction in 2017, the Transformer architecture has become the cornerstone of natural language processing. This article provides an in-depth yet accessible explanation of Transformer’s core mechanisms.
Why Do We Need Transformers?
Before Transformers, RNNs and LSTMs were the mainstream methods for sequence modeling. However, they had several limitations:
- Sequential Computation - Cannot be parallelized, leading to low training efficiency
- Long-range Dependencies - Difficulty capturing long-distance contextual information
- Gradient Issues - Long sequences prone to vanishing gradients
Transformers elegantly solve these problems through the self-attention mechanism.
Core Mechanism: Self-Attention
What is Attention?
The attention mechanism allows the model to attend to all positions in the input sequence when processing each position, dynamically determining which information is more important.
Self-Attention Computation Process
- Linear Transformation: Project input into Query, Key, and Value
- Calculate Similarity: Dot product of Q and K to get attention scores
- Normalization: Apply Softmax to get attention weights
- Weighted Sum: Weight V with the attention weights
Mathematical formula:
$$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $$
Multi-Head Attention
A single attention head may only focus on specific patterns. Multi-head attention allows the model to simultaneously attend to different representation subspaces:
- Multiple Perspectives: Different heads learn different patterns
- Rich Representation: Combines information from multiple heads
- Parameter Sharing: Improves model efficiency
Positional Encoding
Transformers don’t inherently contain positional information, which needs to be injected through positional encoding:
- Absolute Positional Encoding: Generated using trigonometric functions
- Relative Positional Encoding: Encodes relationships between positions
- Learnable Positional Encoding: Learned as parameters
Advantages of Transformers
✅ Parallelization: All positions can be computed simultaneously
✅ Long-range Dependencies: Directly models relationships across any distance
✅ Interpretability: Attention weights provide intuitive explanations
✅ Scalability: Easy to stack and extend
Practical Applications
Transformers have achieved success in multiple domains:
- NLP: BERT, GPT, T5
- CV: ViT, DETR
- Multimodal: CLIP, Flamingo
- Other: AlphaFold, music generation
Summary
Through its innovative self-attention mechanism, the Transformer has revolutionized sequence modeling. Understanding Transformers is key to mastering modern AI technology.
At Luwu.AI Lab, we are exploring applications of Transformers in more domains. Stay tuned for our upcoming research results!
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