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 51–100 of 100 papers

TitleStatusHype
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt LearningCode2
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot LearnersCode0
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningCode4
Towards Unified Prompt Tuning for Few-shot Text ClassificationCode0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization—0
ASCM: An Answer Space Clustered Prompting Method without Answer EngineeringCode0
Label Semantic Aware Pre-training for Few-shot Text ClassificationCode1
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot ClassificationCode1
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification—0
Few-Shot Learning with Siamese Networks and Label TuningCode1
LST: Lexicon-Guided Self-Training for Few-Shot Text Classification—0
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task RepresentationCode0
Label-guided Data Augmentation for Prompt-based Few Shot Learners—0
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification—0
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification—0
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification—0
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text ClassificationCode1
Few-Shot Learning with Siamese Networks and Label Tuning—0
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERCode1
RAFT: A Real-World Few-Shot Text Classification BenchmarkCode1
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification—0
Noisy Channel Language Model Prompting for Few-Shot Text ClassificationCode1
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text ClassificationCode1
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text ClassificationCode1
Distinct Label Representations for Few-Shot Text ClassificationCode1
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text ClassificationCode1
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks—0
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and SystemCode1
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language InferenceCode0
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum LearningCode1
A Neural Few-Shot Text Classification Reality CheckCode1
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network—0
Effective Few-Shot Classification with Transfer Learning—0
Uncertainty-aware Self-training for Few-shot Text Classification—0
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text ClassificationCode2
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications—0
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
Knowledge Guided Metric Learning for Few-Shot Text Classification—0
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language InferenceCode1
Hierarchical Attention Prototypical Networks for Few-Shot Text Classification—0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Few-shot Text Classification with Distributional SignaturesCode1
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Induction Networks for Few-Shot Text ClassificationCode1
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification—0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
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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