Research reveals that in multilingual evaluation, the effectiveness of “large models as referees” heavily depends on the choice of referee model.
A multilingual empirical study published on October 24, 2026, on arXiv found that current mainstream prompting strategies—such as chain-of-thought and role‑playing—lack cross‑model and cross‑language transferability in LLM‑as‑a‑Judge scenarios; the choice of the judge model exerts far greater influence than the prompting strategy itself, and the effectiveness of the strategy can even reverse direction across different judge models.
Why it matters：This study is the first to systematically measure the interaction effects among nine prompting strategies, four open-source evaluation models, 27 languages, and 13 datasets, generating over one million human-validated point-by-point ratings and filling a critical gap between English-centric prompting research and fixed-template multilingual studies.