Fix evaluation bias to unlock better models on Amazon Bedrock
According to AWS. Property Finder engineering teams discovered that evaluation methodology bias blocked objective model comparison.
- • 96.8 percent of evaluation labels were inherited directly from a single model's predictions.
- • Property Finder hand-annotated new images to build an unbiased ground truth dataset.
- • Prompt optimization achieved higher accuracy at an 86 percent lower cost on Amazon Bedrock.
Uncovering Evaluation Bias
The engineering team at Property Finder, a digital real estate application, discovered that 96.8 percent of their evaluation labels had been inherited directly from a single model’s predictions rather than verified by human experts. Their ground truth was systematically skewed toward the incumbent model, making every alternative appear to underperform and blocking objective model comparison.
Building Trusted Ground Truth
To break the cycle, human experts with real domain knowledge performed fresh annotations independently without reference to previous model outputs. The team hand-annotated 356 UAE images and a separate set of Egypt images, utilizing the first 256 UAE images as a development set for tuning while using Micro F1 as the primary metric.
- 1. aws.amazon.comOpen source