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Few-Shot Text Classification
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Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances 1
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning
TARDiS : Text Augmentation for Refining Diversity and Separability
Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification
Label-template based Few-Shot Text Classification with Contrastive Learning
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
Manual Verbalizer Enrichment for Few-Shot Text Classification
Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning
CrossTune: Black-Box Few-Shot Classification with Label Enhancement
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification
Label-Aware Automatic Verbalizer for Few-Shot Text Classification
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance
Emotion-Conditioned Text Generation through Automatic Prompt Optimization
Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification
MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Boosting Few-Shot Text Classification via Distribution Estimation
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
Mask-guided BERT for Few Shot Text Classification
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification
Meta-Learning Siamese Network for Few-Shot Text Classification
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration
Understanding BLOOM: An empirical study on diverse NLP tasks
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