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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 91–100 of 100 papers

Title	Date	Tasks	Status	Hype
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation	Jan 27, 2022	Few-Shot Text ClassificationMeta-Learning	CodeCode Available	0
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation	Jun 3, 2023	Few-Shot Text ClassificationMeta-Learning	CodeCode Available	0
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners	May 18, 2022	Data AugmentationFew-Shot Learning	CodeCode Available	0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection	Aug 27, 2021	Few-Shot Text Classificationintent-classification	CodeCode Available	0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation	Nov 17, 2022	Contrastive LearningData Augmentation	CodeCode Available	0
FPT: Feature Prompt Tuning for Few-shot Readability Assessment	Apr 3, 2024	16kFew-Shot Text Classification	CodeCode Available	0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification	Dec 8, 2023	ClassificationFew-Shot Text Classification	CodeCode Available	0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop	Apr 5, 2018	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	0
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models	Mar 4, 2024	Few-Shot Text Classificationparameter-efficient fine-tuning	CodeCode Available	0
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters	Mar 13, 2023	Few-Shot Text ClassificationMeta-Learning	CodeCode Available	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