Learning Notes on Transformer Architecture: From Self-Attention Mechanism to the Foundation of Modern NLP

Since the publication of Google's 2017 paper Attention Is All You Need, the Transformer architecture has completely transformed the landscape of natural language processing, serving as the foundation for mainstream large language models such as GPT, BERT, and T5, and expanding to multiple AI subfields like computer vision and speech recognition. This article outlines its core technical points to help understand the design philosophy and implementation mechanisms of this revolutionary architecture.

Before the emergence of Transformer, sequence modeling relied on RNN and its variants (LSTM, GRU), but faced problems of gradient vanishing and long-term dependency, and sequential computation limited parallelization; CNN captures local features through sliding windows, which allows parallelization but requires multiple layers to handle long-distance dependencies. The attention mechanism was initially an enhancement component for RNN, but Transformer elevated it to the core.

Self-Attention Mechanism

Each input vector is converted into Query, Key, and Value. Attention scores are calculated via dot product, then weighted summation after softmax normalization, enabling global receptive field and parallel computation.

Multi-Head Attention

Project QKV into multiple low-dimensional subspaces, compute attention independently, then concatenate the results to enhance expressive power and capture various semantic relationships.

Positional Encoding

Self-attention is position-invariant, so explicit positional information needs to be introduced. The original uses sine-cosine encoding, and later variants include learnable embeddings and relative positional encoding.

The original Transformer has an encoder-decoder structure. Subsequent variants: BERT uses only the encoder (suitable for understanding tasks), GPT uses only the decoder (good at generation), and T5 retains the full structure (unified text transformation). Applications have expanded to fields like CV (ViT), speech (Whisper), and protein structure prediction (AlphaFold).

Impact: Highly versatile, it has transformed NLP and multiple AI subfields, becoming a foundational component of general AI. Limitations: The computational complexity of self-attention is proportional to the square of the sequence length, leading to high costs for long sequences; it requires large amounts of data and computing resources, raising concerns about environmental costs and data dependency; interpretability still needs improvement.

1. Start with the original paper Attention Is All You Need, combine it with The Annotated Transformer code annotation tutorial, and implement a simplified version by yourself.
2. Use the Hugging Face Transformers library to practice with pre-trained models.
3. Use tools like BertViz to visualize attention patterns, explore the effects of different positional encodings, and adjust hyperparameters (number of heads, number of layers, etc.) to deepen understanding.