Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild
TL;DR AI
2 min readKey summary
A study of 57 ML evaluation harnesses and 16,560 issues found recurring operational failures across the evaluation pipeline.
The authors built a five-stage harness model and found specification-stage problems were the most common.
Top root causes were unimplemented features, documentation gaps, and missing input validation.
The findings suggest evaluation harnesses have distinct engineering failure modes and should be treated as a separate software engineering concern.
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