LM-CPPF RoBERTa-base

Reported on 4 benchmarks across 2 tasks · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing4 results

Sentiment AnalysisonCR
Accuracy· 2023-05-29
93.3
best: 93.54 (AnglE-LLaMA-7B)
SOTA
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning arXiv:2305.18169
Natural Language InferenceonMultiNLI
Accuracy· 2023-05-29
68.4
best: 85.7 (MT-DNN-SMARTv0)
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning arXiv:2305.18169
Sentiment AnalysisonSST-5 Fine-grained classification
Accuracy· 2023-05-29
54.9
best: 62.27 (Llama-3.3-70B + CAPO)
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning arXiv:2305.18169
Sentiment AnalysisonSST-2 Binary classification
Accuracy· 2023-05-29
93.2
best: 97.5 (T5-11B)
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning arXiv:2305.18169