Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML
Synthetic perturbations appear to offer inexpensive calibration data for LLM evaluators in biomedical ML, where expert review is scarce. Yet a planted mutation key is neither a detector output nor automatically human ground truth. We formalize four distinct ledgers: planted perturbations, independent detector outputs, source-linked human dispositions, and human-added discoveries. We then audit the evaluation design, scoring code, read paths, and current human records of a private synthetic Japanese care-handoff workflow. The factory stored 69 planted error cards across 47 targets. Final review covers 22 targets and contains 22 confirmed imported proposals, 9 rejected proposals, and 79 hum...
| Authors | Sidi Chang, Peiying Zhu |
|---|---|
| Primary category | cs.AI |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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