Few-Shot Learning

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–25 of 2964 papers

Title	Date	Tasks	Status	Hype
Rethinking the Sample Relations for Few-Shot Classification	Jan 23, 2025	ClassificationContrastive Learning	CodeCode Available	7
LLaMA: Open and Efficient Foundation Language Models	Feb 27, 2023	Arithmetic ReasoningCode Generation	CodeCode Available	7
SGLang: Efficient Execution of Structured Language Model Programs	Dec 12, 2023	Few-Shot LearningLanguage Modeling	CodeCode Available	6
GPT-4 Technical Report	Mar 15, 2023	answerability predictionArithmetic Reasoning	CodeCode Available	6
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities	Feb 2, 2024	Acoustic Scene ClassificationAudio captioning	CodeCode Available	5
SymbolicAI: A framework for logic-based approaches combining generative models and solvers	Feb 1, 2024	Few-Shot LearningIn-Context Learning	CodeCode Available	5
Zephyr: Direct Distillation of LM Alignment	Oct 25, 2023	2D Cyclist DetectionFew-Shot Learning	CodeCode Available	5
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression	Oct 10, 2023	Code CompletionFew-Shot Learning	CodeCode Available	5
iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models	Sep 5, 2024	Few-Shot LearningInformation Retrieval	CodeCode Available	4
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities	Aug 14, 2024	Continual LearningFew-Shot Learning	CodeCode Available	4
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation	Apr 5, 2024	Few-Shot LearningScene Segmentation	CodeCode Available	4
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series	Jan 8, 2024	CPUFew-Shot Learning	CodeCode Available	4
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models	Nov 27, 2023	ArticlesConditional Text Generation	CodeCode Available	4
Efficient Few-Shot Learning Without Prompts	Sep 22, 2022	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	4
Flamingo: a Visual Language Model for Few-Shot Learning	Apr 29, 2022	Few-Shot LearningGenerative Visual Question Answering	CodeCode Available	4
Prototypical Verbalizer for Prompt-based Few-shot Tuning	Mar 18, 2022	Contrastive LearningEntity Typing	CodeCode Available	4
What Makes Good In-Context Examples for GPT-3?	Jan 17, 2021	Few-Shot LearningNatural Language Understanding	CodeCode Available	4
Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning	Mar 26, 2025	Few-Shot LearningVisual Reasoning	CodeCode Available	3
The Surprising Effectiveness of Test-Time Training for Few-Shot Learning	Nov 11, 2024	ARCFew-Shot Learning	CodeCode Available	3
Low-Rank Few-Shot Adaptation of Vision-Language Models	May 28, 2024	Few-Shot Learningparameter-efficient fine-tuning	CodeCode Available	3
When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes	Apr 18, 2024	Contrastive LearningFew-Shot Learning	CodeCode Available	3
PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models	Mar 26, 2024	Code CompletionFew-Shot Learning	CodeCode Available	3
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment	Dec 1, 2023	Contrastive LearningFew-Shot Learning	CodeCode Available	3
LLM4Drive: A Survey of Large Language Models for Autonomous Driving	Nov 2, 2023	Autonomous DrivingFew-Shot Learning	CodeCode Available	3
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding	Aug 28, 2023	16kCode Completion	CodeCode Available	3

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All datasets MedConceptsQA DTD FGVC-Aircraft Mini-ImageNet - 5-Shot Learning Mini-Imagenet 5-way (1-shot)Stanford Cars Mini-ImageNet - 1-Shot Learning PubMedQA Caltech101 CaseHOLD EuroSAT Flowers-102

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	gpt-4-0125-preview	Accuracy	61.91	—	Unverified
2	gpt-4-0125-preview	Accuracy	52.49	—	Unverified
3	gpt-3.5-turbo	Accuracy	41.48	—	Unverified
4	gpt-3.5-turbo	Accuracy	37.06	—	Unverified
5	johnsnowlabs/JSL-MedMNX-7B	Accuracy	25.63	—	Unverified
6	yikuan8/Clinical-Longformer	Accuracy	25.55	—	Unverified
7	BioMistral/BioMistral-7B-DARE	Accuracy	25.06	—	Unverified
8	yikuan8/Clinical-Longformer	Accuracy	25.04	—	Unverified
9	PharMolix/BioMedGPT-LM-7B	Accuracy	24.92	—	Unverified
10	PharMolix/BioMedGPT-LM-7B	Accuracy	24.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	67.27	—	Unverified
2	SaSPA + CAL	4-shot Accuracy	48.3	—	Unverified
3	Real-Guidance + CAL	4-shot Accuracy	41.5	—	Unverified
4	CAL	4-shot Accuracy	40.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	Harmonic mean	52.2	—	Unverified
2	CAL	Harmonic mean	35.2	—	Unverified
3	Variational Prompt Tuning	Harmonic mean	34.69	—	Unverified
4	Real-Guidance + CAL	Harmonic mean	34.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BGNN	Accuracy	92.7	—	Unverified
2	TIM-GD	Accuracy	87.4	—	Unverified
3	UNEM-Gaussian	Accuracy	66.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EASY (transductive)	Accuracy	82.75	—	Unverified
2	HCTransformers	5 way 1~2 shot	74.74	—	Unverified
3	HyperShot	Accuracy	53.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	4-shot Accuracy	66.7	—	Unverified
2	Real-Guidance + CAL	4-shot Accuracy	44.3	—	Unverified
3	CAL	4-shot Accuracy	42.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HCTransformers	Acc	74.74	—	Unverified
2	DPGN	Acc	67.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MetaGen Blended RAG (zero-shot)	Accuracy	77.9	—	Unverified
2	CoT-T5-11B (1024 Shot)	Accuracy	73.42	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	68.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	81.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	91.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CovidExpert	AUC-ROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	78.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	65.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	73.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	73.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	78.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	52.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	79	—	Unverified