SOTAVerified

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

Showing 18011850 of 2964 papers

TitleStatusHype
Prompt-free and Efficient Language Model Fine-Tuning0
Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code0
PartImageNet: A Large, High-Quality Dataset of PartsCode1
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples NeurIPS 2021Code1
Ranking Distance Calibration for Cross-Domain Few-Shot Learning0
MDFM: Multi-Decision Fusing Model for Few-Shot Learning0
Re-ranking for image retrieval and transductive few-shot classification0
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?Code0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning0
Statistically and Computationally Efficient Linear Meta-representation Learning0
True Few-Shot Learning with Prompts -- A Real-World Perspective0
Deep Representation Learning with an Information-theoretic Loss0
Coarse-To-Fine Incremental Few-Shot LearningCode0
Deep metric learning improves lab of origin prediction of genetically engineered plasmids0
Few-shot Named Entity Recognition with Cloze Questions0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Adaptive Transfer Learning: a simple but effective transfer learning0
ShufaNet: Classification method for calligraphers who have reached the professional level0
Prompt Combines Paraphrase: Enhancing Biomedical “Pre-training, Prompt and Predicting” Models by Explaining Rare Biomedical Concepts0
On the Multilingual Capabilities of Very Large-Scale English Language Models0
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection0
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection0
Towards Unified Prompt Tuning for Few-shot Learning0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German0
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness0
Inverse is Better! Fast and Accurate Prompt for Slot Tagging0
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction0
Making Small Language Models Better Few-Shot Learners0
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation0
Bi-Matching Mechanism to Combat the Long Tail of Word Sense DisambiguationCode0
Can Explanations Be Useful for Calibrating Black Box Models?0
Cross-domain Named Entity Recognition via Graph Matching0
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models0
Prompt-Guided Few-Shot Event Detection0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis0
Prototypical Verbalizer for Prompt-based Few-shot Tuning0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification0
CoLLIE: Continual Learning of Language Grounding from Language-Image EmbeddingsCode0
Zero-Shot Learning in Named-Entity Recognition with External KnowledgeCode1
Scaling ASR Improves Zero and Few Shot Learning0
Feature Generation for Long-tail ClassificationCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy0
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot LearningCode1
Crowdsourcing with Meta-Workers: A New Way to Save the Budget0
CLUES: Few-Shot Learning Evaluation in Natural Language UnderstandingCode1
An Explanation of In-context Learning as Implicit Bayesian InferenceCode1
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text PairsCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified