CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
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Computer Science > Computation and Language
Title:CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
Abstract: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 larger proprietary LLMs.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.15763 [cs.CL] |
| (or arXiv:2605.15763v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15763
arXiv-issued DOI via DataCite (pending registration)
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