What is Prompt Tuning?
Prompt tuning is a technique designed to enhance the performance of a pre-trained language model without altering its core architecture. Instead of modifying the deep structural weights of the model, prompt tuning adjusts the prompts that guide the model’s response. This method is based on the introduction of “soft prompts,” a set of tunable parameters inserted at the beginning of the input sequence.
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The diagram compares the traditional approach of model tuning with prompt tuning. Notice that in model tuning, each task requires a separate model. On the other hand, prompt tuning utilizes the same foundational model across multiple tasks by adjusting task-specific prompts.
How Does Prompt Tuning Work?
Initialisation of Soft Prompts:
- Soft prompts are artificially constructed tokens added to the model’s input.
- They can be initialized randomly or based on heuristics.
- These prompts are attached to the start of the input data, influencing the model’s processing.
Forward Pass and Loss Evaluation:
- The model processes the combined input (soft prompts + actual input) through its layers.
- The output is compared to the desired outcome using a loss function.
Back propagation:
- Errors are propagated back through the network.
- Only the soft prompt parameters are adjusted, not the network’s weights.
- This process repeats across multiple epochs.
Iteration:
- The cycle of forward pass, loss evaluation, and backpropagation is repeated over multiple epochs.
- Soft prompts learn to shape the input, consistently reducing the loss score and improving task-specific performance.
Benefits of Prompt Tuning
Resource Efficiency:
- Maintains pre-trained model’s parameters unchanged.
- Reduces computational power needed.
- Ideal for resource-constrained environments.
- Avoids deploying separate models for each task.
Rapid Deployment:
- Adjusts only a small set of soft prompt parameters.
- Speeds up the adaptation process.
- Enables quick transitions between tasks, reducing downtime.
Model Integrity and Knowledge Retention:
- Preserves the core architecture and weights of the model.
- Maintains the model’s original capabilities and knowledge.
- Ensures reliability and generalizability across applications.
Task Flexibility:
- Uses a single foundational model for multiple tasks by changing soft prompts.
- Reduces the need to train and maintain separate models.
- Enhances scalability and simplicity in model management.
Reduced Human Involvement:
- Requires less human intervention than prompt engineering.
- Automated optimization of soft prompts minimizes human error.
- Maximizes efficiency by reducing time-consuming manual prompt crafting.
Competitive Performance:
- Can achieve performance levels comparable to fine-tuning, especially in large models.
- Combines high efficiency with strong results as model sizes grow.
Prompt tuning thus offers a highly efficient, flexible, and effective approach to optimizing large language models.
