Papers › Entailment as Few-Shot Learner
Entailment as Few-Shot Learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, Hao Ma
Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. However, their success hinges largely on scaling model parameters to a degree that makes it challenging to train and serve. In this paper, we propose a new approach, named as EFL, that can turn small LMs into better few-shot learners. The key idea of this approach is to reformulate potential NLP task into an entailment one, and then fine-tune the model with as little as 8 examples. We further demonstrate our proposed method can be: (i) naturally combined with an unsupervised contrastive learning-based data augmentation method; (ii) easily extended to multilingual few-shot learning. A systematic evaluation on 18 standard NLP tasks demonstrates that this approach improves the various existing SOTA few-shot learning methods by 12\%, and yields competitive few-shot performance with 500 times larger models, such as GPT-3.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Linguistic Acceptability | CoLA | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 86.4% | #5 of 43 | Archive leaderboard | report |
| Natural Language Inference | QNLI | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 94.5% | #16 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 90.5% | #15 of 90 | Archive leaderboard | report |
| Natural Language Inference | RTE | RoBERTa-large 355M + EFL + UCA | Accuracy | 87.2% | #22 of 90 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Neural Tree Indexers for Text Understanding | % Test Accuracy | 93.1 | #3 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | Neural Tree Indexers for Text Understanding | Parameters | 355 | #3 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | EFL (Entailment as Few-shot Learner) + RoBERTa-large | % Test Accuracy | 93.1 | #4 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | EFL (Entailment as Few-shot Learner) + RoBERTa-large | % Train Accuracy | ? | #4 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | EFL (Entailment as Few-shot Learner) + RoBERTa-large | Parameters | 355m | #4 of 98 | Archive leaderboard | report |
| Paraphrase Identification | Quora Question Pairs | RoBERTa-large 355M + Entailment as Few-shot Learner | F1 | 89.2 | #2 of 31 | Archive leaderboard | report |
| Question Answering | BoolQ | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 86.0 | #15 of 65 | Archive leaderboard | report |
| Semantic Textual Similarity | MRPC | RoBERTa-large 355M + Entailment as Few-shot Learner | F1 | 91.0 | #39 of 45 | Archive leaderboard | report |
| Semantic Textual Similarity | STS Benchmark | RoBERTa-large 355M + Entailment as Few-shot Learner | Pearson Correlation | 0.918 | #11 of 66 | Archive leaderboard | report |
| Sentiment Analysis | CR | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 92.5 | #3 of 9 | Archive leaderboard | report |
| Sentiment Analysis | IMDb | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 96.1 | #5 of 49 | Archive leaderboard | report |
| Sentiment Analysis | MPQA | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 90.8 | #1 of 4 | Archive leaderboard | report |
| Sentiment Analysis | MR | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 92.5 | #2 of 19 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 96.9 | #9 of 87 | Archive leaderboard | report |
| Subjectivity Analysis | SUBJ | RoBERTa-large 355M + Entailment as Few-shot Learner | Accuracy | 97.1 | #3 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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