CS5720 - Week 11
Slide 217 of 220

Fine-tuning Pre-trained Language Models

What is Fine-tuning?

Fine-tuning is the process of taking a pre-trained language model and adapting it to a specific task by training it on task-specific data, leveraging the knowledge it already learned.
Why Fine-tune?

• Transfer Learning - Use general knowledge for specific tasks
• Data Efficiency - Need less task-specific data
• Better Performance - Often beats training from scratch
• Faster Training - Start from a good initialization
Key Insight:
Pre-trained models have learned general language understanding. Fine-tuning adapts this knowledge to your specific needs!

Fine-tuning Process

  • 1️⃣
    Prepare Your Data
    Format data for your specific task
  • 2️⃣
    Select Pre-trained Model
    Choose BERT, GPT, RoBERTa, etc.
  • 3️⃣
    Add Task Head
    Add task-specific output layers
  • 4️⃣
    Train on Task Data
    Fine-tune with smaller learning rate
  • 5️⃣
    Evaluate & Deploy
    Test performance and deploy model

Fine-tuning Strategies

🎯
Full Fine-tuning
Update all model parameters
🔧
LoRA / PEFT
Efficient parameter updates
💡
Prompt Tuning
Optimize prompts, freeze model

Fine-tuning Best Practices

  • Use a smaller learning rate (1e-5 to 5e-5)
  • Warm up the learning rate gradually
  • Monitor for overfitting on small datasets
  • Consider freezing lower layers initially
  • Use appropriate batch sizes for stability
  • Save checkpoints regularly
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Prepared by Dr. Gorkem Kar