Rubric-as-Experts: Case-Specific MQM Rubrics for Translation Error Span Detection
Large language models (LLMs) have shown potential for reference-free span-level translation quality estimation (QE), yet existing approaches based on Multidimensional Quality Metrics (MQM) typically rely on fixed rubric configurations shared across translation instances. However, translation instances often differ substantially in error complexity, ambiguity, and required evaluation granularity, making static rubric allocation suboptimal for span-level error detection. We find that larger MQM subtype spaces improve error coverage but also introduce more false positives, while different translation instances prefer different rubric granularities, suggesting that evaluation spaces should be...
| Authors | Weilu Xu, Yunzhi Shen, Xinye Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 June 2026 |
| Last updated at source | 26 September 2026 |
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