Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
This addresses the problem of manual engineering in prompt-based learning for low-resource scenarios, offering a more automated approach for IR systems, though it is incremental as it builds on existing prompt tuning methods.
The authors tackled the reliance on handcrafted prompts and verbalizers in few-shot learning for pre-trained language models by introducing CP-Tuning, an end-to-end contrastive prompt tuning framework, which outperformed state-of-the-art methods across various language understanding tasks in IR systems.
Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.