# Streamlining Admission with LOR Insights: AI-Based Leadership Assessment in Online Master's Program

arXiv: https://arxiv.org/abs/2508.05513 · HTML: https://mlsanity.com/paper/2508.05513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 111 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Reasoning with Language Model Prompting: A Survey](https://mlsanity.com/paper/2212.09597/) (2022)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Can Large Language Models Reason and Plan?](https://mlsanity.com/paper/2403.04121/) (2024)
- [Bayesian Optimization with Gradients](https://mlsanity.com/paper/1703.04389/) (2017)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [An Empirical Comparison of Pre-Trained Models of Source Code](https://mlsanity.com/paper/2302.04026/) (2023)
- [Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital](https://mlsanity.com/paper/2112.11071/) (2021)
- [A Large Language Model Approach to Educational Survey Feedback Analysis](https://mlsanity.com/paper/2309.17447/) (2023)
- [Weaker Than You Think: A Critical Look at Weakly Supervised Learning](https://mlsanity.com/paper/2305.17442/) (2023)
- [How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature](https://mlsanity.com/paper/1803.10311/) (2018)
- [Using word clouds for fast identification of papers' subject domain and reviewers' competences](https://mlsanity.com/paper/2112.14861/) (2021)

## Abstract (arXiv)

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.