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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 41–50 of 100 papers

Title	Date	Tasks	Status	Hype	Score
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering	May 1, 2022	Few-Shot Text ClassificationNatural Language Inference	CodeCode Available	0	5
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation	Jun 3, 2023	Few-Shot Text ClassificationMeta-Learning	CodeCode Available	0	5
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL	Jul 20, 2024	Few-Shot Text ClassificationQ-Learning	CodeCode Available	0	5
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks	Oct 14, 2024	AllFew-Shot Text Classification	CodeCode Available	0	5
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification	Aug 22, 2019	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	0	5
Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning	Mar 30, 2024	Data AugmentationFew-Shot Text Classification	—Unverified	0	0
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification	Oct 29, 2022	Few-Shot LearningFew-Shot Text Classification	—Unverified	0	0
TARDiS : Text Augmentation for Refining Diversity and Separability	Jan 6, 2025	DiversityFew-Shot Text Classification	—Unverified	0	0
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies	May 9, 2025	ClassificationContrastive Learning	—Unverified	0	0
Towards Unified Prompt Tuning for Few-shot Text Classification	May 11, 2022	ClassificationFew-Shot Learning	—Unverified	0	0

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All datasets RAFT Average on NLP datasets Amazon Counterfeit ODIC 10-way (10-shot)ODIC 10-way (5-shot)ODIC 5-way (10-shot)ODIC 5-way (5-shot)SST-5

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	T-Few	Avg	0.76	—	Unverified
2	Human (crowdsourced)	Avg	0.74	—	Unverified
3	GPT-3	Avg	0.63	—	Unverified
4	AdaBoost	Avg	0.51	—	Unverified
5	GPT-Neo	Avg	0.48	—	Unverified
6	GPT-2	Avg	0.46	—	Unverified
7	BART MNLI zero-shot	Avg	0.38	—	Unverified
8	Plurality-class	Avg	0.33	—	Unverified
9	GPT-3 zero-shot	Avg	0.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SetFit + OCD(5)	Accuracy	0.65	—	Unverified
2	SetFit + OCD	Accuracy	0.64	—	Unverified
3	T-few 3B	Accuracy	0.63	—	Unverified
4	SetFit	Accuracy	0.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SetFit + OCD	Accuracy	0.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Induction Networks	Accuracy	81.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Induction Networks	Accuracy	78.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Induction Networks	Accuracy	88.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Induction Networks	Accuracy	87.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SetFit + OCD	Accuracy	0.48	—	Unverified