Contrastive Learning.prompt

# Contrastive Learning

## Overview

Contrastive learning is a representation learning technique that learns meaningful embeddings by contrasting positive pairs (similar examples) against negative pairs (dissimilar examples). In the context of LLMs, contrastive learning improves sentence embeddings, retrieval quality, and few-shot generalization. Methods like SimCLR, CLIP, and InfoNCE loss have become foundational for learning robust representations.

## Key Concepts

- **Positive/negative pairs**: Defining what constitutes similar vs dissimilar examples
- **InfoNCE loss**: The standard contrastive loss function for representation learning
- **Temperature scaling**: Controlling the sharpness of the similarity distribution
- **Hard negatives**: Mining challenging negative examples for better learning
- **Augmentation strategies**: Creating positive pairs through text transformations
- **Batch construction**: Building effective batches with diverse positive/negative pairs

## Implementation Patterns