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

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Showing 1–10 of 100 papers

TitleStatusHype
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
TARDiS : Text Augmentation for Refining Diversity and Separability—0
Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt—0
Manual Verbalizer Enrichment for Few-Shot Text Classification—0
Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classificationCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
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All datasetsRAFTAverage on NLP datasetsAmazon CounterfeitODIC 10-way (10-shot)ODIC 10-way (5-shot)ODIC 5-way (10-shot)ODIC 5-way (5-shot)SST-5

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Induction NetworksAccuracy78.27—Unverified