Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation

Gili Lior; Tzviel Frostig; Gabriel Stanovsky; Matan Eyal

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arXiv · 2606.15643

Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation

Gili Lior·Tzviel Frostig·Gabriel Stanovsky·Matan Eyal

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Abstract

Multilingual benchmarks are central to evaluating large language models (LLMs) across languages, but they suffer from three issues: exhaustive evaluation scales linearly with the number of languages, automatic translation introduces errors that are easily missed at scale, and some items conflate general and culture-specific knowledge. We address all three with a unified statistical framework, Multilingual-IRT, which extends Item Response Theory with per-language difficulty deviations, split discriminability separating content from language effects, and per-language ability residuals. Fitting Multilingual-IRT on 25 LLMs across 29 languages of MMLU-Pro-X, we show that its fitted parameters support three practical applications: predicting unobserved (item, LLM, language) instances with 11-16% lower binary cross-entropy than the strongest accuracy-based baseline, surfacing candidate translation errors distributed across all 28 non-English languages, whereas accuracy-based baselines concentrate detections in a few languages, and recovering culture-specific items that accuracy-based baselines miss.

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Gili Lior, Tzviel Frostig, Gabriel Stanovsky, Matan Eyal. 2026-06-14. Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation. https://arxiv.org/abs/2606.15643

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