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arXiv Machine Learning By Prithvi Dixit, Pedram Hosseini

Are We Grading Properly? Understanding Failure Modes in Medical Benchmarks

Read the original on arXiv Machine Learning →

The paper investigates how rubric-based grading in medical AI benchmarks can fail when the rubrics themselves are flawed. Using the RIFT taxonomy, the authors analyze two clinical benchmarks—HealthBench Professional and LiveMedBench—and discover significant issues: nearly 30% of criteria on HealthBench Professional are non-atomic and over 65% are misaligned or rigid. They demonstrate that correcting bundled criteria can shift scores by up to 15.9 percentage points, revealing that rubric flaws materially affect evaluation outcomes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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