Smruti: Grammatical Error Correction for Gujarati using LLMs with Non-Parametric Memory

AbstractGrammatical Error Correction (GEC) is a fundamental task in Natural Language Processing that focuses on automatically detecting and correcting grammatical errors in text. In this paper, we present a novel approach for GEC for Gujarati. Gujarati is an Indian language spoken by over 55 million people worldwide. Our approach combines a large language model with non-parametric memory modules to address the low-resource challenge. We have evaluated our system on human-annotated and synthetic datasets. The overall result indicates promising results for Gujarati. The proposed approach is generic enough to be adopted by other languages. Furthermore, we release a publicly available evaluation dataset for Gujarati GEC along with an adapted version of the ERRANT framework to enable error-type-wise evaluation in Gujarati.