Overview
Biz-Tech Analytics has been running a high-throughput human preference labeling pipeline for short-form AI-generated video project for 6+ months with new annotation volume delivered weekly. The engagement produces structured RLHF training data for generative video model alignment. Each data point combines A/B comparative preference judgments with granular flaw annotations across four modalities, enabling fine-grained reward model training and multi-modal evaluation benchmarking.
Annotation Framework
1. 4-Dimensional multi-modal taxonomy
Every video pair was independently evaluated across Prompt Adherence, Video Execution, Audio Execution, and Caption/Text Quality, reducing label noise through single-category flaw attribution and preventing double-penalization of the same root issue.
2. 3-tier severity with escalation rules
Flaws were classified as None, Light, or Severe.
3. Timestamped, auditable annotation output
Each item includes: A/B preference label, per-dimension flaw category, timestamps for every flagged issue, and a rationale anchored to concrete observations.
Annotators apply a creator-mindset frame, evaluating against the prompt writer's intent, not subjective viewer taste.
QA Pipeline
Consensus labeling at 1,500 items / 48 hours
Three independent annotation teams work in parallel on each batch of 1,500 items, with a 48-hour turnaround, enabling inter-annotator agreement (IAA) measurement and majority-vote adjudication on contested items across 12–16 annotation parameters. Batches then pass through an independent QC team that aligns all annotators' feedback before delivery. High-disagreement items are resolved before they reach the training set, maintaining accuracy above 90% throughout.
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