Transformer Architecture Explained: From Attention to Large Language Models

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from the Easy AI series on zhichai.net provides a beginner-friendly explanation of the Transformer architecture. It traces the evolution from RNN/LSTM era (2014-2016) through the 2017 publication of 'Attention Is All You Need' to modern large language models like GPT-4. The article breaks down three core components: the embedding layer that converts tokens into 100-1024 dimensional vectors, the Transformer block containing attention mechanisms, MLP layers, and residual connections, and the output probability layer using linear projection and softmax. It explains how Transformers predict the next word through attention computation and probability distribution selection, with analogies like predicting the next line of classical poetry. Key advantages include parallel processing (hundreds of times faster than RNNs), long-distance memory, and task versatility, while challenges include O(n²) attention complexity, massive training data requirements, and lack of native positional awareness. The piece concludes with the scaling timeline from the original 65M-parameter Transformer through BERT (340M), GPT-2 (1.5B), GPT-3 (175B), to GPT-4 (~1.8T parameters), highlighting improvements in scale, training methods (self-supervised learning, RLHF, instruction tuning), and multimodal capabilities.

Transformer Architecture Explained

*This is a full English translation of the Easy AI tutorial series post on the Transformer architecture.*

1. The Birth of the Transformer

1.1 Timeline

| Period | Stage | Description | |--------|-------|-------------| | 2014–2016 | RNN/LSTM era | Sequence models dominated, but struggled with long sequences | | 2017 | Transformer born | Vaswani et al. published "Attention Is All You Need" | | 2018–2019 | BERT & GPT rise | Pre-trained models built on Transformers achieved great success | | 2020–present | LLM era | GPT-3, GPT-4, ChatGPT, etc. changed the world |

1.2 Three Breakthroughs

1. Attention replaces recurrence

Completely breaks free from RNN's serial processing limitation
Parallel processing, hundreds of times faster

2. Global memory

Directly relates words at any distance in the text
Better understanding of long-context semantics

3. Scalable architecture

A solid foundation for subsequent large models
Supports models with hundreds of billions or trillions of parameters

2. Core Components

2.1 Embedding Layer

The magic factory that turns text into numbers

Tokenization: splits "我爱北京" into ["我", "爱", "北京"] — like breaking a sentence into LEGO bricks
Vector conversion: each token becomes a 100–1024 dimensional numeric vector capturing semantic information
Semantic capture: similar words have nearby vectors, reflecting semantic relationships

2.2 Transformer Block

The intelligent factory for information processing

Attention mechanism: lets each word "hear" from all other words, enabling global awareness
MLP layer: applies non-linear transformations and feature extraction to attention output
Residual connections: help gradient flow, making training of deep networks more stable

2.3 Output Probability Layer

The "voting station" where answers are revealed

Linear layer: converts vectors to vocabulary-sized dimensions (e.g., 512 → 50,000)
Softmax function: converts scores into probabilities between 0 and 1 that sum to 1
Probability selection: picks the most likely next word according to the distribution

3. The Underlying Logic of Next-Word Prediction

3.1 Prediction Process

1. Input analysis: the model receives input text, splits it into tokens, and converts each into vectors 2. Attention computation: computes relevance between words (e.g., "今天" and "天气") to understand context 3. Probability distribution: generates a probability distribution over possible next words using softmax 4. Selection: picks the highest-probability word, using greedy search or sampling

3.2 Intuitive Analogy

Classical poetry analogy: like predicting that "床前明月光" is followed by "疑是地上霜"
Pattern learning: the Transformer learns word-association patterns from massive text corpora
Probabilistic word guessing: achieving highly accurate "word guessing"

4. Pros and Cons

4.1 Three Advantages

| Advantage | Description | Analogy | |-----------|-------------|---------| | Parallel speedup | Hundreds of times faster for 1,000-word sentences, fully utilizing GPU parallelism | RNN is like one ticket queue; Transformer is like 10 windows selling tickets simultaneously | | Long-range memory | Directly relates words at any distance | For "the book Xiaoming bought in Paris three years ago," it directly links "Paris" and "book" | | Universal adaptability | Same architecture works for translation, generation, QA, etc. | Like one machine producing both car parts and appliance parts |

4.2 Three Challenges

| Challenge | Description | Impact | |-----------|-------------|--------| | High compute cost | Attention complexity is O(n²); longer sequences cost more | 1,000 words require ~1 million correlation computations, limiting very long contexts | | Data hunger | Base models require hundreds of millions of words (≈100,000 novels) | May underperform traditional methods on small datasets | | No positional awareness | Natively ignores word order; requires positional encoding | Position-sensitive tasks need special design |

5. From Transformer to LLMs

5.1 Evolution Timeline

| Year | Model | Parameters | Capabilities | |------|-------|-----------|--------------| | 2017 | Original Transformer | 65M | Machine translation, basic text generation | | 2018 | BERT | 340M | Text understanding, sentiment analysis, QA | | 2019 | GPT-2 | 1.5B | Fluent generation, zero-shot learning | | 2020 | GPT-3 | 175B | Code generation, creative writing, complex reasoning | | 2023 | GPT-4 | 1.8T | Multimodal understanding, expert-level QA, complex tasks |

5.2 Three Improvement Directions

1. Scale-up

From ~100M parameters (base Transformer) to GPT-4's ~1.8 trillion
Like upgrading from a "small library" to a "national library"

2. Training optimization

Self-supervised learning: masking words for the model to predict
RLHF: making models better understand humans
Instruction tuning: improving task execution

3. Capability expansion

GPT-4 can explain memes in images
Supports text, image, and audio inputs
Cross-modal reasoning and generation

---

*Source: Easy AI Tutorial series*