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    <abstract>A standard method for evaluating grammatical error correction systems severely underestimates performance, as it compares outputsagainst a small, fixed set of human references, despite the large space of possible valid corrections. Prior research has shown that using aclosest-gold reference – i.e., a human reference generated with respect to the system output rather than the original text – yields more accurate performance estimates. Yet, producing such references for each system individually is costly. We introduce an automated method for generating closest-gold references by prompting a large language model (LLM) with system outputs. We find that performance scores computed using automatic closest-gold references correlate well with human closest-golds, whereas standard reference-based evaluations show weak or no correlation.Building on this insight, we use both fixed human references and closest-gold references generated by Claude and Llama to compare theperformance of supervised models and GPT-4 across 14 benchmarks spanning 12 languages. Consequently, while prior work has shown that GPT-4 appears to lag behind traditional models, we demonstrate that this is due to the failures of the standard evaluation method that systematically underestimates GPT-4 performance more severely than that of supervised models. We show that a more appropriate evaluation approach, based on the closest gold method, reveals that GPT-4 outperforms traditional state-of- the-art models on almost all languages.</abstract>
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%0 Conference Proceedings
%T Toward Robust Evaluation for Multilingual Grammatical Error Correction: Can Large Language Models Replace Human References?
%A Rozovskaya, Alla
%A Roth, Dan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F rozovskaya-roth-2026-toward
%X A standard method for evaluating grammatical error correction systems severely underestimates performance, as it compares outputsagainst a small, fixed set of human references, despite the large space of possible valid corrections. Prior research has shown that using aclosest-gold reference – i.e., a human reference generated with respect to the system output rather than the original text – yields more accurate performance estimates. Yet, producing such references for each system individually is costly. We introduce an automated method for generating closest-gold references by prompting a large language model (LLM) with system outputs. We find that performance scores computed using automatic closest-gold references correlate well with human closest-golds, whereas standard reference-based evaluations show weak or no correlation.Building on this insight, we use both fixed human references and closest-gold references generated by Claude and Llama to compare theperformance of supervised models and GPT-4 across 14 benchmarks spanning 12 languages. Consequently, while prior work has shown that GPT-4 appears to lag behind traditional models, we demonstrate that this is due to the failures of the standard evaluation method that systematically underestimates GPT-4 performance more severely than that of supervised models. We show that a more appropriate evaluation approach, based on the closest gold method, reveals that GPT-4 outperforms traditional state-of- the-art models on almost all languages.
%U https://aclanthology.org/2026.acl-long.2193/
%P 47440-47463

Markdown (Informal)

[Toward Robust Evaluation for Multilingual Grammatical Error Correction: Can Large Language Models Replace Human References?](https://aclanthology.org/2026.acl-long.2193/) (Rozovskaya & Roth, ACL 2026)

• Toward Robust Evaluation for Multilingual Grammatical Error Correction: Can Large Language Models Replace Human References? (Rozovskaya & Roth, ACL 2026)

ACL

• Alla Rozovskaya and Dan Roth. 2026. Toward Robust Evaluation for Multilingual Grammatical Error Correction: Can Large Language Models Replace Human References?. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 47440–47463, San Diego, California, United States. Association for Computational Linguistics.