LMCBert:基于大型语言模型和对比学习的自动学术论文评分模型
IEEE transactions on cybernetics
|April 1, 2025
概括
这项研究介绍了LMCBert,这是自动化学术论文评分 (AAPR) 的新型模型. 通过将大型语言模型 (LLM) 与动量对比学习 (MoCo) 集成,LMCBert提高了预测准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 学术传播学术交流
背景情况:
- 学术论文的接受依赖于资源密集型,容易产生偏见的同行评审过程.
- 自动学术论文评分 (AAPR) 方法存在,但经常使用完整的内容,导致效率低下和冗余.
- 像BERT这样的现有模型由于特定领域的语言差异而与AAPR扎.
研究的目的:
- 开发一个更有效,更准确的自动化系统来预测学术论文的接受.
- 解决现有的AAPR方法的局限性,包括预训练模型的低效和低于最佳性能.
- 提出LMCBert,一种结合大型语言模型 (LLMs) 和势头对比学习 (MoCo) 的新型模型.
主要方法:
- 使用LLM从学术论文中提取核心语义内容,减少冗余.
- 实施势头对比学习 (MoCo) 以优化BERT培训,以改善语义差异化.
- 开发LMCBert模型,整合LLMs和MoCo,以提高AAPR.
主要成果:
- LMCBert有效地提取核心语义信息,提高对学术文本的理解.
- MoCo优化增强了BERT对AAPR的语义表示差异化.
- 经验评估证实了LMCBert在评估数据集上的有效表现.
结论:
- LMCBert提供了一种有效和有效的方法,用于自动化学术论文评分.
- 整合LLMs和MoCo显著提高了预测纸张接受度的准确性.
- 拟议的方法解决了当前自动化学术论文评估技术的关键局限性.
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