Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation 事件

Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation arXiv:2605.24904v1 Announce Type: new Abstract: 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. We address two practical gaps: (i) how well automatic MQM-style error spans from LLM judges and a span-awa