Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling
arXiv:2606.15623v1 Announce Type: cross Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons ($O(n^2)$). While sorting-based methods have reduced this burden to $O(n\log n)$, they still require expensive human judgment for every single comparison. To further improve annotation efficiency, we propose leveraging a Vision-Language Model (VLM) not as an annotator replacement, but as a \emph{question prioritizer} to identify which comparisons genuinely require human judgment. The proposed \text
The increasing cost and complexity of human annotation for AI model training necessitate more efficient methods for data labeling and prioritization.
This research enhances the efficiency of subjective ranking tasks, which are crucial for developing high-quality AI models and improving human-in-the-loop systems.
The proposed method reduces the human annotation burden for ranking tasks, making AI development potentially faster and more cost-effective.
- · AI developers
- · Data annotation companies using VLM prioritization
- · Companies with subjective ranking needs
- · Traditional human-in-the-loop annotation services
Reduced cost and time for data annotation in tasks requiring subjective ranking.
Accelerated development of AI systems that rely on human-validated subjective preferences.
Enhanced overall AI system performance and deployment across various industries due to better quality training data.
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Read at arXiv cs.AI