| linguistic-acceptability-on-cola | RoBERTa-large 355M + Entailment as Few-shot Learner | #5 | Accuracy: 86.4% |
| natural-language-inference-on-qnli | RoBERTa-large 355M + Entailment as Few-shot Learner | #14 | Accuracy: 94.5% |
| natural-language-inference-on-rte | RoBERTa-large 355M + Entailment as Few-shot Learner | #13 | Accuracy: 90.5% |
| natural-language-inference-on-rte | RoBERTa-large 355M + EFL + UCA | #19 | Accuracy: 87.2% |
| natural-language-inference-on-snli | Neural Tree Indexers for Text Understanding | #1 | % Test Accuracy: 93.1Parameters: 355 |
| natural-language-inference-on-snli | EFL (Entailment as Few-shot Learner) + RoBERTa-large | #2 | % Test Accuracy: 93.1% Train Accuracy: ?Parameters: 355m |
| paraphrase-identification-on-quora-question | RoBERTa-large 355M + Entailment as Few-shot Learner | #2 | F1: 89.2 |
| question-answering-on-boolq | RoBERTa-large 355M + Entailment as Few-shot Learner | #13 | Accuracy: 86.0 |
| semantic-textual-similarity-on-mrpc | RoBERTa-large 355M + Entailment as Few-shot Learner | #32 | F1: 91.0 |
| semantic-textual-similarity-on-sts-benchmark | RoBERTa-large 355M + Entailment as Few-shot Learner | #8 | Pearson Correlation: 0.918 |
| sentiment-analysis-on-cr | RoBERTa-large 355M + Entailment as Few-shot Learner | #3 | Accuracy: 92.5 |
| sentiment-analysis-on-imdb | RoBERTa-large 355M + Entailment as Few-shot Learner | #5 | Accuracy: 96.1 |
| sentiment-analysis-on-mpqa | RoBERTa-large 355M + Entailment as Few-shot Learner | #1 | Accuracy: 90.8 |
| sentiment-analysis-on-mr | RoBERTa-large 355M + Entailment as Few-shot Learner | #2 | Accuracy: 92.5 |
| sentiment-analysis-on-sst-2-binary | RoBERTa-large 355M + Entailment as Few-shot Learner | #8 | Accuracy: 96.9 |
| subjectivity-analysis-on-subj | RoBERTa-large 355M + Entailment as Few-shot Learner | #3 | Accuracy: 97.1 |