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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 26–50 of 100 papers

TitleStatusHype
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks—0
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models—0
Boosting Few-Shot Text Classification via Distribution Estimation—0
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer AdaptersCode0
Mask-guided BERT for Few Shot Text Classification—0
Like a Good Nearest Neighbor: Practical Content Moderation and Text ClassificationCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration—0
Understanding BLOOM: An empirical study on diverse NLP tasks—0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering—0
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification—0
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification—0
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks—0
OCD: Learning to Overfit with Conditional Diffusion ModelsCode1
Visual Prompt Tuning for Few-Shot Text Classification—0
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Efficient Few-Shot Learning Without PromptsCode4
Adaptive Meta-learner via Gradient Similarity for Few-shot Text ClassificationCode0
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data AugmentationCode0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding 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