用AI分析推荐信，自动评估在线硕士申请者的领导力。
Streamlining Admission with LOR Insights: AI-Based Leadership Assessment in Online Master's Program
- 结合RoBERTa与LLAMA模型，从推荐信中识别团队合作、沟通等领导力特质。
- 模型在测试数据上达到91.6%的加权F1分数，表现稳定可靠。
- 适合高校招生办和关注领导力评估的STEM领域研究者参考。
推荐信能提供标准化考试之外的候选人能力与经历洞察，但文本量大，人工评审耗时费力。为应对这一挑战并支持招生委员会对申请人职业成长提供建议，本研究提出LORI：LOR Insights，一种基于AI的领导力评估工具，用于分析在线硕士项目申请人的推荐信。通过自然语言处理技术，结合RoBERTa与LLAMA大模型，识别团队协作、沟通、创新等领导力特征。最新版RoBERTa模型在测试数据上实现加权F1分数91.6%、精确率92.4%、召回率91.6%，表现出高度一致性。随着领导力在STEM领域的重要性日益凸显，将LORI融入研究生招生流程，不仅可优化审核效率，还能实现更全面、客观的能力评估。
原文摘要 · Abstract (English)
Letters of recommendation (LORs) provide valuable insights into candidates' capabilities and experiences beyond standardized test scores. However, reviewing these text-heavy materials is time-consuming and labor-intensive. To address this challenge and support the admission committee in providing feedback for students' professional growth, our study introduces LORI: LOR Insights, a novel AI-based detection tool for assessing leadership skills in LORs submitted by online master's program applicants. By employing natural language processing and leveraging large language models using RoBERTa and LLAMA, we seek to identify leadership attributes such as teamwork, communication, and innovation. Our latest RoBERTa model achieves a weighted F1 score of 91.6%, a precision of 92.4%, and a recall of 91.6%, showing a strong level of consistency in our test data. With the growing importance of leadership skills in the STEM sector, integrating LORI into the graduate admissions process is crucial for accurately assessing applicants' leadership capabilities. This approach not only streamlines the admissions process but also automates and ensures a more comprehensive evaluation of candidates' capabilities.
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