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.
@article{arxiv.2605.15763,
title = {CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs},
author = {Kamil Guttmann and Zofia Fraś and Artur Nowakowski and Krzysztof Jassem},
journal= {arXiv preprint arXiv:2605.15763},
year = {2026}
}