When Languages Disagree: Self-Evolving Multilingual LLM Judges
Multilingual LLM-as-a-judge is widely used to evaluate model outputs across languages, but suffers from cross-lingual inconsistency (Fu and Liu, 2025). Existing methods typically treat this inconsistency as noise and mitigate it through voting or aggregation. In this work, we instead show that multilingual inconsistency can provide complementary evaluation signals. Our oracle analysis finds that sampling judgments across languages yields a higher performance upper bound than single-language judging, indicating that different languages potentially include complementary judgments. Motivated by this finding, we propose SEMJ, a self-evolving multilingual judge that leverages cross-lingual inc...
| Authors | Xiyan Fu, Wei Lu |
|---|---|
| Primary category | cs.CL |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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