Unraveling the Efficacy of In-Context Learning in Indonesian Grammatical Error Correction

Grammatical error correction (GEC) is of great importance in natural language processing (NLP). However, due to limited language resources, research on the Indonesian GEC remains scarce. In this paper, we propose an InDonesian In-cOntext-guided grammaticaL Error CorrecTion (IDIOLECT) method, aimed at enhancing the performance of large language models (LLMs) on Indonesian GEC task. Specifically, we calculate sentence similarity to select suitable in-context learning (ICL) demonstrations for each sample in the training set and test set, thereby aiding the model in more effectively identifying and correcting grammatical errors. This study further investigates the effects of ICL configurations, demonstration ordering, and demonstration quantity on model performance. The results indicate that the proposed method effectively improves the performance of LLMs in Indonesian GEC task.