Advancing Student Writing Through Automated Syntax Feedback

TL;DR

This paper introduces a specialized dataset and fine-tuned large language models to improve students' syntactic skills through automated feedback, demonstrating significant enhancements in syntax correction capabilities.

Contribution

The study presents a new dataset and fine-tuning approach for LLMs to effectively support student learning of English syntax.

Findings

Fine-tuned LLMs outperform baseline models in syntax correction

The dataset improves LLMs' understanding of syntactic nuances

Enhanced models assist students in identifying and fixing errors

Abstract

This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students. Recognizing the challenges faced by learners in mastering syntactic nuances, we introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax among these students. Leveraging the capabilities of Large Language Models (LLMs) such as GPT3.5-Turbo, Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, and Mistral-7B-Instruct-v0.2, this work embarks on a comprehensive fine-tuning process tailored to the syntax improvement task. Through meticulous evaluation, we demonstrate that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors. The findings not only highlight the effectiveness of the proposed dataset in elevating the performance of LLMs for syntax enhancement but also illuminate a promising path for utilizing advanced language models to support language acquisition efforts. This research contributes to the broader field of language learning technology by showcasing the potential of LLMs in facilitating the linguistic development of Students.