Category:
Category:
Self-Supervision
Category:
Model Training & Optimization
Definition
Training models on unlabeled data by extracting labels automatically.
Explanation
Self-supervision allows models to learn patterns, structure, and reasoning without human-labeled datasets. Models create labels through masked prediction, next-token prediction, or contrastive learning. This technique powers modern LLMs and enables low-cost custom training.
Technical Architecture
Unlabeled Data → Self-Supervised Task → Training → Updated Model
Core Component
Masking engine, prediction head, contrastive loss
Use Cases
Pretraining, domain adaptation, representation learning
Pitfalls
Poor data quality leads to degraded models; limited interpretability
LLM Keywords
Self Supervised Learning, SSL, LLM Pretraining
Related Concepts
Related Frameworks
• Instruction Tuning
• Fine-Tuning
• Synthetic Data
• SSL Training Pipeline