Lecture 12 — Encoder, Decoder, and the Truth About How LLMs Are Trained
~4–5 hours (core understanding lecture)
🧠 Why This Lecture Exists
Almost everyone uses LLMs.
Very few understand how they are actually built.
Common confusion:
- “Is ChatGPT encoder–decoder?”
- “Why only decoder?”
- “What does freezing weights really mean?”
- “How does multimodal fit into this?”
- “What exactly am I training when I fine-tune?”
This lecture answers all of that — clearly, from first principles.
🧩 The Original Transformer (2017)
The original Transformer had two parts:
Encoder → Decoder
Encoder
- Reads the input
- Understands meaning
- Produces representations
Decoder
- Generates output tokens
- Uses attention + autoregression
This was designed for:
- Machine Translation
- Summarization
- Seq2Seq tasks
🧠 Encoder: What Is It Really Doing?
Encoder properties:
- Sees the entire input at once
- Bidirectional attention
- Builds rich representations
- Does not generate text
Examples:
- BERT
- RoBERTa
- ViT (vision encoder)
- Audio encoders
Encoders understand. They don’t speak.
🧠 Decoder: What Is It Really Doing?
Decoder properties:
- Generates tokens one by one
- Causal (masked) attention
- Autoregressive
- Can reason, plan, and explain
Examples:
- GPT
- LLaMA
- Mistral
- Qwen
Decoders speak, reason, and act.
❓ Why ChatGPT Is Decoder-Only
Key insight:
If you want open-ended generation, you only need a decoder.
Reasons:
- Decoder can read context (prompt)
- Decoder can generate indefinitely
- Encoder is not required for generation
- Simpler architecture
- Scales better
So ChatGPT is:
Text → Decoder → Next Token → Next Token → ...
🧠 Decoder-Only Training (GPT Style)
Training objective:
Predict the next token
"I love deep" → predict "learning"
This single objective leads to:
- Language understanding
- Reasoning
- Code generation
- Planning
Understanding emerges from generation.
🧩 Encoder–Decoder Models (Still Important!)
Encoder–decoder models are still used when:
- Input ≠ output
- Strong alignment is required
- Input is very long or structured
Examples:
- T5
- FLAN-T5
- Whisper (audio → text)
- Translation systems
🧠 Multimodal LLMs: The Hybrid Truth
Most multimodal LLMs are:
Encoder (image/audio/video)
↓
Projection / Adapter
↓
Decoder-only LLM
Examples:
- CLIP → LLaMA
- ViT → GPT
- Audio encoder → LLM
Multimodal models are encoder–decoder systems, but the decoder is still the brain.
🔗 Why Encoders Are Usually Frozen
Encoders:
- Pretrained on massive data
- Expensive to retrain
- General-purpose
So we often:
- ❄️ Freeze encoder
- 🔧 Train adapter / projector
- 🧠 Fine-tune decoder lightly
This saves:
- Compute
- Data
- Stability
🧠 What Is Actually Trained? (Very Important)
Pretraining
- Train all weights
- Massive data
- Extremely expensive
Fine-tuning
- Train some weights
- Task-specific data
Instruction tuning
- Train decoder to follow instructions
- Often freezes most layers
🧩 Freezing Strategies
| Strategy | What Moves |
|---|---|
| Full fine-tune | Everything |
| Freeze encoder | Decoder only |
| LoRA | Small rank matrices |
| Adapters | Tiny modules |
| Prompt tuning | No weights |
Most real-world systems do NOT full fine-tune.
🐍 Example: Freezing Encoder
for param in vision_encoder.parameters():
param.requires_grad = False
Then train:
- Projection layer
- LLM LoRA weights
🧠 LoRA Explained Simply
LoRA:
- Injects low-rank matrices
- Keeps original weights frozen
- Learns task-specific behavior
Benefits:
- Cheap
- Stable
- Shareable
- Reversible
LoRA is how the world fine-tunes LLMs today.
❓ Why Not Encoder-Only LLMs?
Encoder-only models:
- Cannot generate freely
- Need a decoder for output
- Not conversational
That’s why:
- BERT ≠ ChatGPT
- ViT ≠ multimodal assistant
🧠 Mental Model (Remember This Forever)
| Role | Model Type |
|---|---|
| Understand | Encoder |
| Reason | Decoder |
| Speak | Decoder |
| Act | Decoder + Tools |
| See | Vision Encoder |
| Hear | Audio Encoder |
🧪 Student Knowledge Check (Hidden)
Q1 — Objective
Why can ChatGPT work without an encoder?
Answer
Because a decoder can read context and generate text autoregressively.
Q2 — MCQ
Which model is encoder-only?
A. GPT B. LLaMA C. BERT D. ChatGPT
Answer
C. BERT
Q3 — MCQ
What is usually frozen in multimodal LLMs?
A. Decoder B. Encoder C. Tokenizer D. Loss function
Answer
B. Encoder
Q4 — Objective
Why use LoRA instead of full fine-tuning?
Answer
To reduce cost, preserve knowledge, and improve stability.
Q5 — Objective
Who is the “brain” of a multimodal LLM?
Answer
The decoder-only LLM.
🌱 Final Reflection
If intelligence emerges from predicting the next token, what does that say about human thinking?
That reasoning may emerge from sequence prediction guided by experience.
✅ Final Takeaways (Burn This In)
- ChatGPT is decoder-only
- Encoders understand, decoders generate
- Multimodal = encoders + decoder brain
- Freezing is strategy, not weakness
- Fine-tuning is about what to move