AI Training Data Annotation & Quality Optimization Project
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RLHFPerformed high-quality AI training data annotation for text dialogue, including RLHF positive and negative sample labeling, content ranking, and preference alignment across tens of thousands of standardized entries. Conducted data cleaning, screening, and verification to remove duplicate, invalid, and low-quality samples while correcting labeling errors and unifying labeling standards for consistency. Participated in platform quality inspection and random sampling, completing required modifications and documenting common issues to improve labeling efficiency and accuracy over time. • Labeled text dialogue intents and boundary/ambiguous cases following standardized specifications • Annotated RLHF preference signals (positive/negative samples) and scored/ranked responses for alignment • Verified content compliance, deduplicated data, and corrected semantic/labeling inconsistencies • Achieved stable annotation accuracy above 98.5% with zero violation records and no major rework