CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonstrate that smaller, open-source LLMs (<30B parameters) are a viable, cost-effective and privacy-preserving alternative. Using a single-pass prompting strategy, our models simultaneously generate quality scores, MQM error annotations, suggested error corrections, and full post-editions. Our analysis shows these models achieve highly competitive system-level correlations with human judgments that outperform traditional neural metrics, fine-tuned models, and human inter-annotator agreement, effectively approximating the capabilities of much larg...
| Authors | Kamil Guttmann, Zofia Fraś, Artur Nowakowski et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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