← 研踪 PaperTrailarXiv:2611.14861 · v1
Is Jev a Good Multilingual Judge?
首次提交 2026-11-11 08:21
版本更新 2026-11-11 08:21
雷达观测 2026-11-13 10:09
摘要
Generative large language models are widely used for multilingual text evaluation, yet whether decision models that directly output judgment probabilities can serve as multilingual judges has received limited attention. We evaluate Jev against generative LLM judges on four multilingual benchmarks with human annotations, covering up to 47 language varieties and three evaluation formats: binary decisions, pairwise comparisons, and graded ratings. At approximately $1/31$ to $1/61$ of GPT-6.1's evaluation cost, Jev is competitive on most multilingual judging tasks, but falls behind GPT-6.1 on knowledge-intensive answer verification. Further analysis shows that Jev is more likely to accept incorrect answers in low-resource languages than in English and that its probability outputs help identify errors across tasks. Based on these findings, we offer recommendations on evaluation instructions, confidence-based selection, and model routing, providing empirical guidance for using Jev as a multilingual judge.
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v1 · 2026-11-11 08:21 · 最新观测版本
Is Jev a Good Multilingual Judge?
Generative large language models are widely used for multilingual text evaluation, yet whether decision models that directly output judgment probabilities can serve as multilingual judges has received limited attention. We evaluate Jev against generative LLM judges on four multilingual benchmarks with human annotations, covering up to 47 language varieties and three evaluation formats: binary decisions, pairwise comparisons, and graded ratings. At approximately $1/31$ to $1/61$ of GPT-6.1's evaluation cost, Jev is competitive on most multilingual judging tasks, but falls behind GPT-6.1 on knowledge-intensive answer verification. Further analysis shows that Jev is more likely to accept incorrect answers in low-resource languages than in English and that its probability outputs help identify errors across tasks. Based on these findings, we offer recommendations on evaluation instructions, confidence-based selection, and model routing, providing empirical guidance for using Jev as a multilingual judge.
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