How We Taught an AI Agent to Fix Our Training Data

Combining high-quality datasets for model training can lead to poorer performance due to annotation inconsistencies, as discovered by researchers working on a layout detection model. The issue arises when datasets with ostensibly compatible labels encode differing spatial assumptions, confusing the model with conflicting definitions. To tackle this, a label harmonization workflow was developed, involving a VLM agent that processes and reconciles annotations into a consistent standard before training. This approach improved model performance significantly across various metrics, demonstrating that coherent supervision is crucial for effective model learning. The findings suggest that annotation inconsistency is a pervasive issue in fine-tuning models with independently curated data sources, highlighting the importance of supervision consistency in the training process.

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