As large language models are increasingly deployed across diverse real-world applications, extending automated evaluation beyond English has become a critical challenge. Existing evaluation approaches are predominantly English-focused, and adapting them to other languages is hindered by the scarcity and cost of human-annotated judgments in most languages. We introduce a decomposition-based evaluation framework built around a Universal Criteria Set (UCS). UCS consists of a shared, language-agnostic set of evaluation dimensions, producing an interpretable intermediate representation that supports cross-lingual transfer with minimal supervision. Experiments on multiple faithfulness tasks across languages and model backbones demonstrate consistent improvements over strong baselines without requiring target-language annotations.
@article{arxiv.2603.18557,
title = {Cross-Lingual LLM-Judge Transfer via Evaluation Decomposition},
author = {Ivaxi Sheth and Zeno Jonke and Amin Mantrach and Saab Mansour},
journal= {arXiv preprint arXiv:2603.18557},
year = {2026}
}