Large Language Models (LLMs) like GPT, LLaMA, and BERT are trained on massive datasets. However, to adapt them for specific tasks or domains, two main strategies are used:
- Fine-tuning (modifying model parameters)
- Prompt-tuning (modifying input prompts, keeping parameters frozen).
1. Fine-tuning
Definition
Fine-tuning involves continuing the training process of a pre-trained model on a smaller, task-specific dataset.
- The weights of the model are updated.
- Often uses supervised learning.
Types of Fine-tuning
- Full Fine-tuning
- All model parameters updated.
- High accuracy, but requires massive compute + storage.
- Parameter-efficient Fine-tuning (PEFT)
- Update only a small subset of parameters.
- Examples: LoRA (Low-Rank Adaptation), Prefix-tuning, Adapter layers.
Advantages
✅ Achieves high task accuracy.
✅ Strong adaptation to domain-specific data (e.g., medical, legal, finance).
✅ Useful when large labeled datasets are available.
Limitations
❌ Expensive (compute + memory).
❌ Needs retraining for each new task/domain.
❌ Risk of catastrophic forgetting (model loses general knowledge).
2. Prompt-tuning
Definition
Prompt-tuning adapts a pre-trained LLM without changing its parameters. Instead, it learns special prompt embeddings that guide the model.
- The LLM stays frozen.
- Only a small number of trainable embeddings (prompts) are optimized.
- Can be soft prompts (learned vectors, not human-readable text).
Types of Prompt-tuning
- Manual Prompt Engineering
- Handcrafting text prompts.
- Example: “Summarize this text in one sentence: …”.
- Prefix / Soft Prompt Tuning
- Learnable embeddings prepended to the input.
- Efficient → requires <1% of parameters to be trained.
Advantages
✅ Lightweight (low compute cost).
✅ Fast training → only prompt embeddings updated.
✅ Works well in multi-task learning (different prompts for different tasks).
Limitations
❌ Typically less accurate than full fine-tuning.
❌ Limited for tasks requiring deep adaptation.
❌ May depend on model size (works better on very large models).
3. Key Differences (Comparison Table)
| Aspect | Fine-tuning | Prompt-tuning |
|---|---|---|
| Model Parameters | Updated (partly or fully) | Frozen (no updates) |
| Training Cost | High (GPU, storage) | Low (efficient) |
| Data Needs | Large datasets | Small datasets |
| Flexibility | Strong domain adaptation | Easy multi-task switching |
| Performance | Usually higher | Moderate |
| Risk | Catastrophic forgetting | Limited expressivity |
| Best for | Domain-specific tasks (e.g., medical NLP) | Quick adaptation, resource-constrained settings |
4. Example Applications
- Fine-tuning
- Legal document classification.
- Medical report summarization.
- Chatbots specialized for finance.
- Prompt-tuning
- Sentiment analysis with small data.
- Style transfer (formal vs. informal tone).
- Multi-task setups (translation + summarization).
5. Emerging Trend – Hybrid Approaches
- Fine-tuning + Prompt-tuning → best of both worlds.
- Example: LoRA + Prompt-tuning → low compute cost + higher accuracy.
- Increasingly popular in enterprise AI deployment.
✅ In summary:
- Use Fine-tuning when you have large domain-specific datasets, resources, and need deep accuracy.
- Use Prompt-tuning when you want fast, cost-efficient adaptation, especially in multi-task scenarios.