P-Tuning v2: Prompt Tuning Can Compare to Fine-tuning

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Univers…
Language models are the core of natural language processing (NLP). Large language models, such as GPT-3, have demonstrated the effectiveness of “fine-tuning,” where a new task-specific layer is added to the pre-trained model to adapt it to different NLP tasks. However, fine-tuning is computationally expensive and often requires a large amount of data, which makes it less appealing for low-resource tasks and domains.
Recently, prompt-tuning (P-tuning), another pre-training strategy that replaces the task-specific layer with a static task prompt, has attracted increasing attention. PTuning has shown promise in making better use of pre-trained models for a wide range of NLP tasks without fine-tuning.
In this study, we further investigate P-tuning and find that it can achieve comparable performance to fine-tuning on a wide range of NLP tasks. We also conduct experiments to compare P-tuning with fine-tuning in terms of efficiency and data efficiency. The results show that P-tuning is more efficient and data efficient than fine-tuning.
Our experiments focus on the GPT-3 model and nine NLP tasks, including sentiment analysis, question answering, text generation, and more. We design task prompts for each task and train them on the same dataset as the fine-tuning baseline. The results show that P-tuning can achieve performance comparable to fine-tuning, sometimes even better.
Moreover, we analyze the behavior of P-tuning during training and find that it can effectively learn task-specific representations by dynamically adjusting the attention distribution over the pre-trained model’s hidden states. This behavior is similar to fine-tuning but with much less fine-tuning parameters and computational cost.
Overall, our study demonstrates the effectiveness and efficiency of P-tuning, suggesting its potential as an alternative to fine-tuning for a wide range of NLP tasks. We believe that further research and development in this area will lead to more exciting applications and achievements in the future.