Open-Sourcing JudgeCalibrationKit: Calibrating LLM-as-a-Judge with Swift
DaveAppleInc · reddit · 2026-08-01
A developer shared an open-source Swift package, JudgeCalibrationKit, designed to address the calibration of LLM-as-judge evaluations after scores are generated.
Key Features:
- Provides various statistical metrics (Cohen's κ, Krippendorff's α) to compare judge ratings with human annotations.
- Anti-masking Mechanism: Uses confidence interval gates and clustered bootstrapping to prevent aggregate high scores from hiding severe discrepancies in specific data slices (e.g., locale).
- Transparency: Undefined statistics remain explicit .unavailable(reason) instead of becoming zero or NaN.
- Independence: The statistics core has no third-party dependencies, builds on Linux, and optionally supports Apple's Evaluations framework.
The author is seeking feedback from developers experienced with real-world LLM judge workflows on handling multiple human raters, abstentions, and release thresholds.
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