# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 2 Related Works
### Korean Grammatical Error Correction  
Deep learning paradigms for Grammatical Error Correction (GEC) often treat the problem akin to machine translation: the objective is to \"translate\" an erroneous sentence into its grammatically correct counterpart. This is predominantly achieved through sequence-to-sequence frameworks, leveraging noising encoders and denoising decoders [24; 25; 26].  
There has been a noticeable surge in GEC research tailored to high-resource languages. Novel approaches have emerged, such as the sequence tagging models built on transformer architectures for both error detection and rectification [27]. Educative interfaces, aiming to assist language learners, now encompass enhanced interpretability with explicative texts and examples delineating reasons behind each correction [28]. Techniques like Self-Supervised Curriculum Learning gauge data complexity via training losses, optimizing models for better performance [29]. Moreover, contrastive learning methodologies show promise, especially for domains characterized by sparse errors [30]. On the efficiency frontier, aggressive decoding has been shown to boost model speed by rapidly decoding a multitude of tokens [31].  
However, the application of these advanced methods often hinges on the availability of parallel corpora, encapsulating pairs of incorrect and correct sentences. A significant hurdle for the Korean language is the absence of such publicly accessible datasets. To circumvent this, research efforts in low-resource, including Korean, are concentrated on creating pseudo-parallel corpora autonomously, eliminating the need for manual intervention. In the Chinese GEC task, automatic noise insertion is conducted by incorporating visually or phonologically resembled characters to generate erroneous sentences [32]. Additionally, Ref. [33] focuses on the Swedish GEC task. The cornerstone of this approach is the automatic noise generation technique, wherein designed noise functions are imposed on mono corpora to generate pseudo parallel sets [34]. Noise is typically introduced in the form of grapheme-to-phoneme deviations, misplacements of spacing and punctuation, and pronunciation errors, among others. Nonetheless, a consistent challenge remains: different studies employ varied error definitions, and when training and testing datasets are derived from pseudo-generated data, there's a propensity for overlapping error types. This overlap can compromise the objectivity of evaluations, making unbiased research a daunting task.  
Notably, while there's been a surge of advancements in various methodologies for GEC, a significant gap remains: the exploration of LLMs within this domain. LLMs, with their expansive parameter sets and generalized training, have transformed various segments of the NLP landscape. To assess the potential utilization of LLMs in the GEC task, research has been conducted across various languages such as English, Chinese, Japanese, and others [35; 36; 37]. Yet, their application to K-GEC remains largely uncharted territory. To the best of our knowledge and based on our literature review, there has not been a concerted effort to harness the capabilities of LLMs in Korean, such as ChatGPT [10] or GPT-4 [16], for this task. This paper seeks to bridge this gap, venturing into the application and analysis of LLMs for K-GEC, probing their potential in redefining error correction mechanisms for morphologically rich languages like Korean.