Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction

arXiv:2606.15416v1 Announce Type: new 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 grammat

Source: arXiv cs.CL — read the full report at the original publisher.

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