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 51–75 of 100 papers

TitleStatusHype
Understanding BLOOM: An empirical study on diverse NLP tasks—0
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks—0
Visual Prompt Tuning for Few-Shot Text Classification—0
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications—0
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning—0
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification—0
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance—0
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions—0
CrossTune: Black-Box Few-Shot Classification with Label Enhancement—0
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification—0
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering—0
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
Effective Few-Shot Classification with Transfer Learning—0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization—0
Emotion-Conditioned Text Generation through Automatic Prompt Optimization—0
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt—0
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
Few-Shot Learning with Siamese Networks and Label Tuning—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network—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