Data labeling — the process of annotating raw data with the correct answers a supervised model should learn from — is genuinely unglamorous, labor-intensive work, and it’s also foundational to whether a model actually works well or not.
What Labeling Actually Looks Like in Practice
Image classification: "This image contains a dog" (bounding box optional) Sentiment analysis: "This review text is: negative" Named entity recognition: "Apple[ORG] released the new iPhone[PRODUCT] today" RLHF preference data: "Response A is genuinely better than Response B"
Why “Garbage In, Garbage Out” Applies So Directly Here
A model can only genuinely learn what its labels actually teach it — inconsistent labeling (different human annotators genuinely disagreeing on ambiguous cases), systematic labeler bias, or simple, honest labeling errors all directly, measurably degrade final model quality, frequently in ways that are genuinely hard to diagnose after the fact, since the resulting model behaves confidently even when trained on flawed labels.
Quality Control Techniques
- Multiple annotators per example — genuine disagreement between independent labelers flags authentically ambiguous cases worth reviewing further, and majority voting reduces the impact of any single labeler’s individual errors
- Gold-standard test examples — inserting known-correct examples into a labeling batch specifically to measure and monitor each individual labeler’s genuine accuracy over time
- Clear, genuinely detailed labeling guidelines — reducing subjective, ambiguous interpretation as much as realistically possible for inherently judgment-based labeling tasks
The rise of RLHF specifically has created genuine new demand for a particular kind of labeling — human preference rankings between candidate model outputs, rather than simple, more traditional classification labels — a real, distinct skill and genuinely more nuanced, judgment-heavy task than earlier, more mechanical labeling work.