A Comparative Analysis on Large Language Models for Automated Text Error Correction

Automated text error correction using large language models (LLMs) is a critical research area in natural language processing (NLP). Through pre-training and fine-tuning, LLMs can effectively correct errors at multiple levels, including spelling, grammar, syntax, and semantics, thereby improving overall accuracy. However, existing models face challenges in adapting to personalized correction needs, understanding complex semantic structures, and balancing efficiency with accuracy in real-time scenarios. This paper presents a comparative analysis of the error correction capabilities of GPT, BERT, and other LLMs using evaluation metrics such as precision and accuracy, exploring their effectiveness across domains including education, office automation, and content creation, and providing insights into their practical deployment and limitations. The findings contribute to the ongoing development of intelligent text correction systems, guiding future improvements in adaptability and performance.