Quantifying the Impact of Translation Errors on Multilingual LLM Evaluation
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-aware QE baseline (xCOMET-XXL) match expert human span annotations on benchmark translations, and (ii) how strongly translation errors (as opposed to source-side issues in the English original) explain accuracy drops on translated benchmarks. We find that span agreement is non-trivial on naturally occurring benchmark transl...
| Authors | Klaudia-Doris Thellmann, Bernhard Stadler, Michael Färber et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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