SOTAVerified

Few-Shot Text Classification

Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances 1

Papers

Showing 1–10 of 100 papers

TitleStatusHype
Efficient Few-Shot Learning Without PromptsCode4
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningCode4
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt LearningCode2
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text ClassificationCode2
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Distinct Label Representations for Few-Shot Text ClassificationCode1
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot ClassificationCode1
A Neural Few-Shot Text Classification Reality CheckCode1
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text ClassificationCode1
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1T-FewAvg0.76—Unverified
2Human (crowdsourced)Avg0.74—Unverified
3GPT-3Avg0.63—Unverified
4AdaBoostAvg0.51—Unverified
5GPT-NeoAvg0.48—Unverified
6GPT-2Avg0.46—Unverified
7BART MNLI zero-shotAvg0.38—Unverified
8Plurality-classAvg0.33—Unverified
9GPT-3 zero-shotAvg0.29—Unverified
#ModelMetricClaimedVerifiedStatus
1SetFit + OCD(5)Accuracy0.65—Unverified
2SetFit + OCDAccuracy0.64—Unverified
3T-few 3BAccuracy0.63—Unverified
4SetFitAccuracy0.62—Unverified
#ModelMetricClaimedVerifiedStatus
1SetFit + OCDAccuracy0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1Induction NetworksAccuracy81.64—Unverified
#ModelMetricClaimedVerifiedStatus
1Induction NetworksAccuracy78.27—Unverified
#ModelMetricClaimedVerifiedStatus
1Induction NetworksAccuracy88.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Induction NetworksAccuracy87.16—Unverified
#ModelMetricClaimedVerifiedStatus
1SetFit + OCDAccuracy0.48—Unverified