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

Benchmark Model Rank Results
sentiment-analysis-on-crLM-CPPF RoBERTa-base#2Accuracy: 93.3
sentiment-analysis-on-sst-5-fine-grained-classificationLM-CPPF RoBERTa-base#6Accuracy: 54.9
linguistic-acceptability-on-colaLM-CPPF RoBERTa-base#39Accuracy: 14.1%
sentiment-analysis-on-sst-2-binary-classificationLM-CPPF RoBERTa-base#40Accuracy: 93.2
natural-language-inference-on-qnliLM-CPPF RoBERTa-base#41Accuracy: 70.2%
natural-language-inference-on-multinliLM-CPPF RoBERTa-base#55Accuracy: 68.4