一种基于LLM的混合方法,用于增强自动化作文评分
1AI Empowered, Santiago, Chile. john.atkinson@uai.cl.
Scientific reports
|April 25, 2025
概括
使用浅层数据的自动化作文评分 (AES) 模型是有限的. 我们的混合大型语言模型方法通过整合多种语言特征来提高论文质量评估,以更好地评估连贯性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 教育技术的教育技术
背景情况:
- 传统的自动化作文评分 (AES) 系统依赖于浅层词汇特征 (例如,单词频率,句子长度).
- 这些方法往往忽略了文本结构和语义的关键方面,导致对文章连贯性和整体质量的评估不足.
- 需要更复杂的AES方法来捕捉更深层次的语言细微差别.
研究的目的:
- 为自动化作文评分 (AES) 提出和评估一种混合方法,该方法整合了来自不同语言水平的多种功能.
- 通过结合结构和语义信息来解决基于浅特征的AES的局限性.
- 开发一个更准确,更有效的工具来评估学生的写作.
主要方法:
- 开发了一种混合AES模型,将浅层词汇特征与更深层次的语言特征 (结构,语义) 结合起来.
- 使用大型语言模型 (LLM) 作为混合方法的核心.
- 在标准论文数据集上进行实验,以验证模型的性能.
主要成果:
- 拟议的基于LLM的混合AES模型显著优于传统的浅层基于特征的方法.
- 该模型还在论文评分方面超过了纯神经网络模型的性能.
- 证明了整合各种语言特征的有效性,以提高一致性和质量评估.
结论:
- 一种利用多个语言水平的混合方法,由大型语言模型提供动力,为自动化作文评分提供了一种卓越的方法.
- 这项研究促进了准确和有效的工具的发展,用于自动评估学生的写作能力.
- 整合语义和结构特征对于克服现有AES系统的局限性至关重要.
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