Rank Reversal in Multilingual LLM Judges: A Label-Free Double-Centering Calibrator
Multilingual LLM judges produce different evaluator-backbone rankings depending on the prompt language: on an eight-language Agent-as-a-Judge benchmark, the top-ranked backbone alternates across English, Arabic, Chinese, Hindi, Japanese, Spanish, Turkish, and Swahili, and 7 of 15 backbone pairs show statistically significant pairwise rank reversal. We treat this as a measurement problem. The multilingual judge score decomposes additively into task difficulty, backbone skill, and a language-backbone interaction term, the last of which is recoverable without human labels by double-centering the cell-mean score matrix. We make this estimator (\textbf{Consensus-Based Calibration}, CBC) explic...
| Authors | Alhasan Mahmood, Samir Abdaljalil, Hasan Kurban |
|---|---|
| Primary category | cs.CL |
| Published | 23 August 2026 |
| Last updated at source | 6 September 2026 |
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