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    <abstract>Large Language Models (LLMs) have demonstrated great potential as evaluators of NLG systems, allowing for high-quality, reference-free, and multi-aspect assessments. However, existing LLM-based metrics suffer from two major drawbacks: reliance on proprietary models to generate training data or perform evaluations, and a lack of fine-grained, explanatory feedback. We introduce OpeNLGauge, a fully open-source, reference-free NLG evaluation metric that provides accurate explanations based on individual error spans. OpeNLGauge is available as a two-stage ensemble of larger open-weight LLMs, or as a small fine-tuned evaluation model, with confirmed generalizability to unseen tasks, domains and aspects. Our extensive meta-evaluation shows that OpeNLGauge achieves competitive correlation with human judgments, outperforming state-of-the-art models on certain tasks while maintaining full reproducibility and providing explanations more than twice as accurate.</abstract>
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%0 Conference Proceedings
%T OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs
%A Kartac, Ivan
%A Lango, Mateusz
%A Dusek, Ondrej
%Y Flek, Lucie
%Y Narayan, Shashi
%Y Phương, Lê Hồng
%Y Pei, Jiahuan
%S Proceedings of the 18th International Natural Language Generation Conference
%D 2025
%8 October
%I Association for Computational Linguistics
%C Hanoi, Vietnam
%F kartac-etal-2025-openlgauge
%X Large Language Models (LLMs) have demonstrated great potential as evaluators of NLG systems, allowing for high-quality, reference-free, and multi-aspect assessments. However, existing LLM-based metrics suffer from two major drawbacks: reliance on proprietary models to generate training data or perform evaluations, and a lack of fine-grained, explanatory feedback. We introduce OpeNLGauge, a fully open-source, reference-free NLG evaluation metric that provides accurate explanations based on individual error spans. OpeNLGauge is available as a two-stage ensemble of larger open-weight LLMs, or as a small fine-tuned evaluation model, with confirmed generalizability to unseen tasks, domains and aspects. Our extensive meta-evaluation shows that OpeNLGauge achieves competitive correlation with human judgments, outperforming state-of-the-art models on certain tasks while maintaining full reproducibility and providing explanations more than twice as accurate.
%U https://aclanthology.org/2025.inlg-main.19/
%P 292-337

Markdown (Informal)

[OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs](https://aclanthology.org/2025.inlg-main.19/) (Kartac et al., INLG 2025)

• OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs (Kartac et al., INLG 2025)

ACL

• Ivan Kartac, Mateusz Lango, and Ondrej Dusek. 2025. OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs. In Proceedings of the 18th International Natural Language Generation Conference, pages 292–337, Hanoi, Vietnam. Association for Computational Linguistics.