# Towards Harnessing the Most of ChatGPT for Korean Grammatical Error Correction
## 1 Introduction  
Grammatical error correction systems identify and rectify errors in sentences. The challenge is pronounced in Korean due to its morphological richness and agglutinative nature [1; 2; 3]. The primary method for Korean error correction is rule-based, where specific error types are mapped to correction rules [4]. While effective in preserving sentence structure, this approach has its pitfalls: it is labor-intensive and rigid, failing to catch errors outside predefined rules. To mitigate this, statistical approaches have been introduced, basing error judgments on corpus-derived probabilities [5; 6]. However, these require substantial corpora to achieve satisfactory performance. Deep learning has recently emerged as a potent tool for grammatical correction, notably overcoming the challenges of prior approaches. Methods have been introduced specifically for Korean, enabling error correction model training without parallel data [1; 7; 8]. These primarily rely on noise injection processes that yield pseudo-parallel corpora from unlabeled mono corpora [1; 9]. Typically, a sentence from the mono corpus serves as the target, with the noised variant acting as the source for training sequence-to-sequence models. With the advent of large language models (LLMs) like ChatGPT [10], tasks previously researched in parallel in the NLP domain now converge under a single model. The model's capabilities emerge based on the input prompt, aligning outputs to user objectives [11; 12; 13; 14; 15]. In NLP, a \"prompt\" refers to the input query or statement. \"Prompt engineering\" focuses on designing and leveraging these prompts effectively. With this technique, one can precisely define the input prompt and provide clear task-specific information, enabling the model to yield more accurate outputs. Prompt engineering is actively employed in prominent LLMs like ChatGPT and GPT-4 [16]. From an LLM perspective, prompts areuser-defined commands for desired outcomes, and prompt engineering aims to identify optimal prompts to enhance output quality. Current research focuses on devising the best prompts to harness LLM's inherent capabilities. This includes studies on adversarial prompting, adversarial prompt detectors, prompt managers like TaskMatrix.AI [17], and parameter-efficient fine-tuning (PEFT) approaches such as P-Tuning and LoRA [18]. Despite advances in deep learning and LLM-based methods, two critical challenges persist. First, the lack of a standardized grammatical error correction dataset results in inconsistent model evaluations. Researchers often resort to creating test sets from arbitrary corpus samples, leading to non-uniform benchmarks across studies, potentially skewing performance metrics [19; 20]. Second, the variability in error types defined across research further compromises assessment reliability. Without a precise error typology standard, a model's efficacy might be misjudged based on the specific test set used, either under or overestimating its performance [2]. Therefore, [PERSON]. [21; 22; 23] highlighted the shortcomings in data creation and evaluation in prior Korean grammatical correction research. They introduced an error-type classification system and, leveraging this, constructed the inaugural gold-standard test set for Korean Grammatical Error Correction (K-GEC), termed K-NCT. In light of the recent advancements and shifts in the field, this study aims to embark on a comprehensive exploration and validation of the performance of the K-GEC task, specifically grounded on the K-NCT test set, using the ChatGPT framework. We endeavor to critically analyze, benchmark, and interpret the results within the broader context of the evolution of NLP methodologies. Our goal is not only to measure raw performance metrics but also to understand the intricate nuances, potential challenges, and areas of improvement that the K-NCT dataset might present when paired with advanced models like ChatGPT.