StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
This addresses the unreliability of prompt tuning for few-shot learning in real-world applications, though it is incremental as it builds on existing prompting methods.
The paper tackled the instability in few-shot learning with prompting by separating hard and soft prompts and using contrastive learning, resulting in a 6.97% accuracy improvement and a 1.92 reduction in standard deviation compared to state-of-the-art methods.
Large language models have shown their ability to become effective few-shot learners with prompting, revolutionizing the paradigm of learning with data scarcity. However, this approach largely depends on the quality of prompt initialization, and always exhibits large variability among different runs. Such property makes prompt tuning highly unreliable and vulnerable to poorly constructed prompts, which limits its extension to more real-world applications. To tackle this issue, we propose to treat the hard prompt and soft prompt as separate inputs to mitigate noise brought by the prompt initialization. Furthermore, we optimize soft prompts with contrastive learning for utilizing class-aware information in the training process to maintain model performance. Experimental results demonstrate that \sysname outperforms state-of-the-art methods by 6.97% in accuracy and reduces the standard deviation by 1.92 on average. Furthermore, extensive experiments underscore its robustness and stability across 8 datasets covering various tasks. Codes are available at https://github.com/lccc0528/Stable/tree/main.