用专有数据集微调大模型，自动帮学生纠正语法错误。
Advancing Student Writing Through Automated Syntax Feedback
- 用自建数据集Essay-Syntax-Instruct微调LLM，专注语法纠错。
- 微调后模型在语法问题识别与修正上表现显著提升。
- 适合语言学习者、教育科技研究者及智能辅导系统开发者。
本研究强调语法反馈在提升学生语法能力中的关键作用。针对学习者掌握语法细微差别时面临的挑战，我们构建了一个名为Essay-Syntax-Instruct的专用数据集，旨在增强学生对英语语法的理解与应用。利用GPT3.5-Turbo、Llama-2-7b-chat-hf、Llama-2-13b-chat-hf和Mistral-7B-Instruct-v0.2等大语言模型（LLMs），本研究开展了针对语法改进任务的全面微调。通过严谨评估，结果表明微调后的LLMs在解决语法相关问题方面表现出显著提升，可有效帮助学生识别并修正语法错误。研究不仅验证了所提数据集在提升LLM语法能力方面的有效性，也为利用先进语言模型支持语言习得提供了新路径。该工作推动了语言学习技术的发展，展示了LLMs在促进学生语言能力提升方面的潜力。
原文摘要 · Abstract (English)
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.
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