CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs
The paper titled 'CompactQE' presents a new approach to translation quality estimation using smaller, open-source language models. These models are shown to be effective and cost-efficient alternatives to larger proprietary models, addressing data privacy concerns. The authors demonstrate that their models achieve competitive results in quality assessment compared to traditional methods and human evaluations.
- ▪Current translation quality estimation relies heavily on large, proprietary language models, which raise privacy issues.
- ▪The authors propose using smaller, open-source language models with less than 30 billion parameters as a viable alternative.
- ▪Their models can generate quality scores, error annotations, and suggested corrections in a single pass, outperforming traditional metrics.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15763 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Summary source text | contentText |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2605.15763 (cs) [Submitted on 15 May 2026] Title:CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs Authors:Kamil Guttmann, Zofia Fraś, Artur Nowakowski, Krzysztof Jassem View a PDF of the paper titled CompactQE: Interpretable Translation Quality Estimation via Small Open-Weight LLMs, by Kamil Guttmann and 3 other authors View PDF HTML (experimental) 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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