Here’s a breakdown of when to use fine-tuning vs. prompt tuning, along with the pros and cons of each approach:
What is Fine-Tuning?
- Involves: Updating all or a large subset of a language model’s parameters (weights) with a new dataset that’s specific to your desired task.
- Goal: Adapt the model’s overall knowledge and behavior to better suit your application.
- Example: Fine-tuning a general-purpose language model on a dataset of legal documents to make it better at generating legal text.
What is Prompt Tuning?
- Involves: Carefully crafting text prompts with instructions and examples to guide the language model’s output without changing its underlying parameters.
- Goal: Elicit the desired behavior from the existing model for a specific task.
- Example: Providing a prompt like “Answer the following question in a sarcastic tone: Question: What’s the best thing about Mondays? Answer: …”
When to Choose Fine-tuning
- Large Task Differences: Your task is significantly different from what the pre-trained language model was originally trained on.
- Abundant Data: You have a sizable dataset of labeled examples specific to your task.
- Accuracy is Paramount: You need the highest possible accuracy and are willing to invest time and resources.
- Long-Term Solution: You need a solution that will persist and serve as a foundation for future tasks.
When to Choose Prompt Tuning
- Minor Adjustments: Your task is similar to how the model was originally trained but requires subtle adjustments.
- Limited Data: You have a small number of examples or no dataset at all.
- Speed and Flexibility: You need a quick solution and want the ability to easily experiment with different prompts.
- Preserving Model Capabilities: You don’t want to risk sacrificing the model’s performance on other tasks.
Pros and Cons
Fine-tuning Approach :Higher potential accuracy, persistent changesResource-intensive, time-consuming, risk of overfitting
Prompt Tuning :Fast, flexible, low data requirements ,may not be as accurate, consistency can be an issue
Hybrid Approaches
Increasingly, the lines between fine-tuning and prompt tuning are blurring. Techniques like:
- Parameter-Efficient Fine-tuning (PEFT): Updates only a smaller subset of parameters for efficiency.
- Prompting within Fine-tuning: Using well-formulated prompts as part of the fine-tuning dataset to improve results.
Key Considerations
Ultimately, the best choice depends on:
- The complexity of your task
- The amount and quality of your data
- Available resources (time and computation power)
- Your desired level of accuracy
