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Found 3 papers, 1 papers with code
StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation
Large language models have shown their ability to become effective few-shot learners with prompting, revoluting the paradigm of learning with data scarcity.
Does DetectGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective.
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