Extending Item Response Theory for Efficient and Meaningful Multilingual Evaluation

arXiv:2606.15643v1 Announce Type: new 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 effect

Source: arXiv cs.CL — read the full report at the original publisher.

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