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 1–50 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
A Neural Few-Shot Text Classification Reality CheckCode1
Label Semantic Aware Pre-training for Few-shot Text ClassificationCode1
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceCode1
Like a Good Nearest Neighbor: Practical Content Moderation and Text ClassificationCode1
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and SystemCode1
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text ClassificationCode1
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot ClassificationCode1
Few-Shot Learning with Siamese Networks and Label TuningCode1
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text ClassificationCode1
Few-shot Text Classification with Distributional SignaturesCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Distinct Label Representations for Few-Shot Text ClassificationCode1
MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text ClassificationCode1
Noisy Channel Language Model Prompting for Few-Shot Text ClassificationCode1
OCD: Learning to Overfit with Conditional Diffusion ModelsCode1
Induction Networks for Few-Shot Text ClassificationCode1
RAFT: A Real-World Few-Shot Text Classification BenchmarkCode1
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum LearningCode1
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERCode1
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text ClassificationCode1
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text ClassificationCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot LearnersCode0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
ASCM: An Answer Space Clustered Prompting Method without Answer EngineeringCode0
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language ModelsCode0
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language InferenceCode0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class ClassificationCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
FPT: Feature Prompt Tuning for Few-shot Readability AssessmentCode0
Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classificationCode0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer AdaptersCode0
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference TransformationCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Evolutionary Verbalizer Search for Prompt-based Few Shot Text ClassificationCode0
Towards Unified Prompt Tuning for Few-shot Text ClassificationCode0
Adaptive Meta-learner via Gradient Similarity for Few-shot Text ClassificationCode0
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data AugmentationCode0
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies—0
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification—0
Uncertainty-aware Self-training for Few-shot Text Classification—0
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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
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