LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning

Benchmark Model Rank Results
linguistic-acceptability-on-colaLM-CPPF RoBERTa-base#34Accuracy: 14.1%
natural-language-inference-on-multinliLM-CPPF RoBERTa-base#54Accuracy: 68.4
natural-language-inference-on-qnliLM-CPPF RoBERTa-base#35Accuracy: 70.2%
sentiment-analysis-on-crLM-CPPF RoBERTa-base#2Accuracy: 93.3
sentiment-analysis-on-sst-2-binaryLM-CPPF RoBERTa-base#38Accuracy: 93.2
sentiment-analysis-on-sst-5-fine-grainedLM-CPPF RoBERTa-base#6Accuracy: 54.9