공부하면서 정리하는 포스트

Industry Scale Semi-Supervised Learning for Natural Language Understanding

Meta-tuning Language Models to Answer Prompts Better

CTRLsum: TOWARDS GENERIC CONTROLLABLE TEXT SUMMARIZATION

How Many Data Points is a Prompt Worth?

How Can We Know What Language Models Know?

CoCon: A Self-Supervised Approach For Controlled Text Generation

Self-training Improves Pre-training for Natural Language Understanding

SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

Data Augmentation using Pre-trained Transformer Models

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

EDA : Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

CTRL: A CONDITIONAL TRANSFORMER LANGUAGE MODEL FOR CONTROLLABLE GENERATION

Do Not Have Enough Data? Deep Learning to the Rescue!

Blank Language Models
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