  Large language models have shown their ability to become effective few-shot
learners with prompting, revoluting 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 7.20% in accuracy and reduces the standard
deviation by 2.02 on average. Furthermore, extensive experiments underscore its
robustness and stability across 7 datasets covering various tasks.
