Encode Errors: Representational Retrieval of In-Context Demonstrations for Multilingual Grammatical Error Correction
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 semantica...
| Authors | Guangyue Peng, Wei Li, Wen Luo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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