Label Distillation Pattern

Context

You have a large unlabeled corpus and need training data for a supervised learning task. Manual labeling at scale is prohibitively expensive. The task has structure you can exploit, some samples are easier to label reliably than others.

Problem

How do you construct a high-quality training set from unlabeled data without labeling everything?

Solution

Progressively filter the corpus to samples where labels can be inferred with high confidence, then use this distilled subset for training.

Step 1: High-recall candidate identification

Apply a filter that captures most true positives, even at the cost of many false positives. For NER, this might be proper-noun detection. For highlight classification, it might be timestamp proximity to user-created clips.

Tune for sensitivity over precision, you want to minimize false negatives at this stage.

Step 2: Cross-reference with known sources

Match candidates against external data: known entity lists, frequency patterns, existing metadata. This converts some candidates to reliable labels.

Example: From 2M sentences → 120K with proper-noun candidates → 80K with labels confirmed via known entity lists.

Step 3: Train and iterate

Train a model on the distilled subset. Use this model to label more of the original corpus with confidence thresholds. Add high-confidence predictions to training data. Repeat.

Each iteration improves the model, which improves labeling coverage, which improves the model.

Consequences

Benefits:

• Dramatically reduces labeling cost
• Training data quality is high (only confident labels)
• Iterative improvement bootstraps from small initial set
• Human effort focuses on validation, not labeling from scratch

Tradeoffs:

• Distilled set may not cover full distribution (sampling bias)
• Requires domain knowledge to design good filters
• Iteration adds complexity to training pipeline
• Final model may underperform on edge cases excluded from distillation

Example: PII Detection

Stage	Samples	Task
Raw corpus	~2,000,000 sentences N/A
After proper-noun filter	120,000	Candidate identification
After entity-list matching	80,000	Reliable labels
Human verification needed	~4,000	”Moderate task” validation

The burden drops from 2M difficult labeling tasks to 4K moderate validation tasks.

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