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    <abstract>Grammatical Error Correction (GEC) involves detecting and correcting the wrong usage of grammar. While large language models (LLMs) with in-context learning (ICL) capabilities have shown significant progress on various natural language processing (NLP) tasks, their few-shot performance on GEC remains suboptimal. This is mainly due to the challenge of retrieving suitable in-context demonstrations that capture error patterns instead of semantic similarity. In this paper, we demonstrate that LLMs can inherently capture information related to grammatical errors through their internal states. From these states, we extract the Grammatical Error Representation (GER), an informative and semantically neutral encoding of grammatical errors. Our novel GER-based retrieval method significantly boosts performance in ICL settings on multilingual GEC datasets, improving the precision of correction. For high-resource languages, our results on 8B-sized open-source models match those of closed-source models such as Deepseek2.5 and GPT-4o-mini. For low-resource languages, our F₀.5 scores surpass the baseline by up to a factor of 1.2. This method provides a more precise and resource-efficient solution for multilingual GEC, offering a promising direction for interpretable GEC research.</abstract>
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%0 Conference Proceedings
%T Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction
%A Peng, Guangyue
%A Li, Wei
%A Luo, Wen
%A Wang, Houfeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F peng-etal-2025-encode
%X Grammatical Error Correction (GEC) involves detecting and correcting the wrong usage of grammar. While large language models (LLMs) with in-context learning (ICL) capabilities have shown significant progress on various natural language processing (NLP) tasks, their few-shot performance on GEC remains suboptimal. This is mainly due to the challenge of retrieving suitable in-context demonstrations that capture error patterns instead of semantic similarity. In this paper, we demonstrate that LLMs can inherently capture information related to grammatical errors through their internal states. From these states, we extract the Grammatical Error Representation (GER), an informative and semantically neutral encoding of grammatical errors. Our novel GER-based retrieval method significantly boosts performance in ICL settings on multilingual GEC datasets, improving the precision of correction. For high-resource languages, our results on 8B-sized open-source models match those of closed-source models such as Deepseek2.5 and GPT-4o-mini. For low-resource languages, our F₀.5 scores surpass the baseline by up to a factor of 1.2. This method provides a more precise and resource-efficient solution for multilingual GEC, offering a promising direction for interpretable GEC research.
%R 10.18653/v1/2025.findings-acl.1090
%U https://aclanthology.org/2025.findings-acl.1090/
%U https://doi.org/10.18653/v1/2025.findings-acl.1090
%P 21166-21180

Markdown (Informal)

[Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction](https://aclanthology.org/2025.findings-acl.1090/) (Peng et al., Findings 2025)

• Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction (Peng et al., Findings 2025)

ACL

• Guangyue Peng, Wei Li, Wen Luo, and Houfeng Wang. 2025. Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21166–21180, Vienna, Austria. Association for Computational Linguistics.