CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs

Kamil Guttmann, Zofia Fraś, Artur Nowakowski, Krzysztof Jassem


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.
Anthology ID:
2026.eamt-1.9
Volume:
Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
Month:
June
Year:
2026
Address:
Tilburg, The Netherlands
Editors:
Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
96–113
Language:
URL:
https://aclanthology.org/2026.eamt-1.9/
DOI:
Bibkey:
Cite (ACL):
Kamil Guttmann, Zofia Fraś, Artur Nowakowski, and Krzysztof Jassem. 2026. CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 96–113, Tilburg, The Netherlands. European Association for Machine Translation.
Cite (Informal):
CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs (Guttmann et al., EAMT 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.eamt-1.9.pdf