Data Labeling and Annotation
What is Data Labeling? Data labeling is the process of adding informative tags or annotations to raw data, creating the ground truth that supervised machine learning models learn from.
Types of Annotations
Text Annotation
| Type | Use Case | Example |
|---|---|---|
| Classification | Sentiment analysis | Positive/Negative/Neutral |
| NER | Information extraction | [PERSON: John] works at [ORG: Google] |
| Sequence labeling | POS tagging | The/DT cat/NN sat/VBD |
| Pairwise | Preference learning | Response A > Response B |
Image Annotation
- Bounding boxes: Object detection
- Segmentation masks: Pixel-level labeling
- Keypoints: Pose estimation
- Polygons: Instance segmentation
Annotation Quality Metrics
Inter-Annotator Agreement
| Metric | Formula | Good Threshold |
|---|---|---|
| Cohen's Kappa | Agreement beyond chance | >0.8 |
| Krippendorff's Alpha | Multi-rater reliability | >0.8 |
| Fleiss' Kappa | Multiple annotators | >0.7 |
Quality Control Strategies 1. Gold standard questions: Test annotators against known answers 2. Overlap: Have multiple annotators label same item 3. Auditing: Regular review of annotation samples 4. Training: Calibration sessions for new annotators
Annotation Platforms
| Platform | Type | Highlights |
|---|---|---|
| Scale AI | Commercial | High quality, expensive |
| Labelbox | SaaS | Good UI, collaborative |
| Label Studio | Open source | Self-hosted, flexible |
| Prodigy | Commercial | Active learning, efficient |
| Amazon SageMaker Ground Truth | AWS | Integrated with AWS ML |
Best Practices for LLM Data
- Create detailed annotation guidelines with examples
- Include edge cases and ambiguous scenarios
- Measure and report annotator agreement
- Version control your annotation guidelines
- Use synthetic data generation to augment limited labels
data labelingannotationgtquality
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