Measuring the Cross-Lingual Comprehension Gap: How the language of the evidence shapes what language models understand
Language models are often evaluated as though capabilities demonstrated in English remain equally available when the same content is presented in other languages. Traditional multilingual benchmarks rarely isolate language while holding content, question, reference answer, model, and evaluation unit constant. We define the Cross-Lingual Comprehension Gap (CLCG) as the reduction in response quality when the same content and question are presented in a target language rather than in English. Using ParallelQA-18, a professionally human-translated parallel corpus, we evaluate five models from five laboratories on a stratified sample of 150 articles across 18 languages (English reference; Port...
| Authors | Rafael da Silva, Jeff Eicher |
|---|---|
| Primary category | cs.CL |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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