Prompt-Tuning with Perturbation-Based Regularizer

Standard prompt tuning can be unstable. Even when you run the same training process (where you only update the soft prompt parameters and keep the rest of the model frozen) with identical settings, the learned soft prompts can differ significantly between runs. These differences can, in turn, lead to varied performance on your task.

In prompt tuning, you're not updating the entire model; instead, you only train a small set of parameters known as the soft prompts (continuous vectors). Because these represent only a tiny fraction of the overall parameters, the optimization becomes highly sensitive. Even slight differences in how these few parameters are initialized or minor noise in the training data can cause the optimization process to converge to different solutions.

To address this challenge, Prompt Tuning with Perturbation-based Regularizer (PTP) adds controlled variations during training. These are deliberate, small changes introduced into the training data or the prompt embeddings.

For example:

• Random Noise-Based Perturbations (PTP-RN): Tiny amounts of random noise are added to the prompt embeddings, or some words in the text are randomly masked.
• Adversarial Perturbations (PTP-ADV): Small, worst-case modifications designed to challenge the model.

By training on both the original and perturbed versions of the data, PTP helps the soft prompts become more robust. The optimization process then finds a solution that is less sensitive to small random fluctuations, leading to a more stable and consistent prompt that generalizes better to new data.

Why Should You Use PTP?

• Training is more reliable: PTP stabilizes the training process, ensuring consistent model performance.
• Better performance: The model learns more effectively, improving accuracy on real-world tasks.
• Works across different NLP problems: PTP enhances text classification, sentiment analysis, and other NLP applications.
• Minimal extra effort: PTP integrates easily into existing prompt tuning setups without major changes.

Conclusion

While standard prompt tuning is a powerful method for adapting large language models, its instability can lead to unpredictable results. Prompt Tuning with Perturbation-based Regularizer (PTP) addresses this issue by adding small, controlled variations during training. This regularization technique smooths out the loss landscape, leading to more stable training, better generalization, and higher accuracy with minimal extra effort. PTP makes prompt tuning a more reliable and effective approach for a wide range of NLP tasks.

Footnotes

1. Chen, L., Huang, H., & Cheng, M. (2023). PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer. https://arxiv.org/abs/2305.02423 ↩ ↩²