Research on Grammar Improvement and Automatic Generation in English Writing Based on GPT-4

With the rapid development of artificial intelligence technology, large language models are increasingly used in the field of natural language processing, especially in the automated evaluation and improvement of English writing. This paper proposes a method for improving and automatically generating English writing grammar based on GPT-4 and generative adversarial networks (GAN). This method uses the powerful language generation ability of GPT-4 to automatically generate grammatically compliant and fluent text by fine-tuning student writing samples. In order to further optimize the quality of the generated text, we introduce GAN to conduct adversarial training on the generated results, thereby improving the grammatical accuracy, language fluency and overall generation quality of the text. The experiment uses writing data from undergraduate students, covering a variety of writing types and grammatical error types, including tense, subject-verb agreement, spelling errors, etc. By comparing with traditional grammar checker systems (such as Grammarly) and BERT models, the results show that the model based on the combination of GPT-4 and GAN is significantly superior to other methods in terms of grammatical accuracy ($92.5 \%$), fluency ($89.3 \%$) and generation quality ($91.2 \%$). This study not only demonstrates the application effect of combining GPT-4 and GAN in improving the automation of English writing, but also provides new ideas and technical support for intelligent writing tutoring systems. Future work will focus on further optimizing the model, improving its real-time feedback capabilities, and exploring cross-language application scenarios to expand the application prospects of this technology in different languages and writing tasks.