用大模型实现按文体精准评分与改进建议
Automated Genre-Aware Article Scoring and Feedback Using Large Language Models
- 融合BERT与Chat-GPT理解文本内容与结构
- 在多数据集上优于传统方法，尤其擅长特征评分
- 为不同写作文体生成个性化反馈，适合教育场景
本文提出一种先进的智能文章评分系统，不仅能评估写作整体质量，还能根据文章类型提供基于特征的细致评分。系统结合预训练BERT模型与大型语言模型Chat-GPT，深入理解文本内容与结构，从而给出全面评价及针对性改进建议。实验结果表明，该系统在多个公开数据集上优于传统评分方法，尤其在特征级评估中表现突出，能更准确反映不同文体文章的质量。此外，系统可生成个性化反馈，帮助用户提升写作能力，凸显了自动化评分技术在教育领域的实用价值。
原文摘要 · Abstract (English)
This paper focuses on the development of an advanced intelligent article scoring system that not only assesses the overall quality of written work but also offers detailed feature-based scoring tailored to various article genres. By integrating the pre-trained BERT model with the large language model Chat-GPT, the system gains a deep understanding of both the content and structure of the text, enabling it to provide a thorough evaluation along with targeted suggestions for improvement. Experimental results demonstrate that this system outperforms traditional scoring methods across multiple public datasets, particularly in feature-based assessments, offering a more accurate reflection of the quality of different article types. Moreover, the system generates personalized feedback to assist users in enhancing their writing skills, underscoring the potential and practical value of automated scoring technologies in educational contexts.
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