AI RESEARCH
Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation
arXiv CS.CL
•
ArXi:2605.24904v1 Announce Type: new Machine-translated benchmarks are widely used to assess the multilingual capabilities of large language models (LLMs), yet translation errors in these benchmarks remain underexplored, raising concerns about the reliability and comparability of multilingual evaluation.