REU Site: Recent Advances in Natural Language Processing

这个项目的目标是让渴望在计算机科学，特别是人工智能和机器学习(AI-ML)领域追求更高职业生涯的有动力的本科生在职业生涯早期接触到积极的研究经验。该项目将在科罗拉多大学科罗拉多斯普林斯分校开发一个本科生暑期研究体验(REU)网站，每年吸引来自全国各地的10名参与者参与前沿研究，主题包括大型语言模型的使用和安全，以及使用量子计算的新范式来更好地解决自然语言处理中的问题。学生将编写软件来解决有趣和及时的问题，撰写论文发表，并将接触到申请研究生院的程序。该提案还将侧重于从研究机会有限的机构、女性和代表性不足的少数族裔培养未来的计算机科学家。这些研究经验将鼓励这些学生在未来的职业生涯中成为学术和非学术环境中富有成效的研究人员。参与的学生将通过有重点的讲座、对最新出版物的讨论和个性化阅读向他们介绍最新的进展，如深度学习、大型语言模型和量子计算。特别是，学生们将了解深度学习领域的最新创新，包括以变形金刚为灵感的架构，例如来自变形金刚的双向编码器表示法(BERT)和生成式预培训变形金刚(GPT)系列，这些创新在最近一段时间为许多多年来一直是研究重点的问题带来了持续改善的结果。学生将对新问题进行实践研究，使用基于变压器的深度学习体系结构和量子计算方法进行实验，并通过书面论文和演示文稿交流他们的结果。示例项目将涉及控制大型语言模型的生成；保护语言模型免受攻击；以及使用量子计算来解决诸如词性标记和命名实体识别等任务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this project is to expose motivated undergraduates who aspire to pursue advanced careers in Computer Science, in particular in Artificial Intelligence and Machine Learning (AI-ML), to active research experience early in their careers. This project will develop a summer Research Experience for Undergraduates (REU) site at the University of Colorado, Colorado Springs, to engage ten participants per year from around the country in cutting-edge research on topics such as the use and security of Large Language Models, and the use of the new paradigm of Quantum Computing to better solve problems in Natural Language Processing. The students will write software to solve interesting and timely problems, write papers for publication, and will be exposed to procedures for applying for graduate school. The proposal will also focus on training future computer scientists from institutions with limited research opportunities, women, and under-represented minorities. The research experience will encourage these students to be productive researchers in academic and non-academic environments during their future careers.The participating students will be introduced to the latest advances such as Deep Learning, Large Language Models, and Quantum Computing via focused lectures, discussion of latest publications, and personalized readings. In particular, the students will learn about latest innovations in Deep Learning, including Transformer-inspired architectures, such as the Bidirectional Encoder Representations from Transformers (BERT) and the Generative Pre-Trained Transformers (GPT) series, that have ushered in a recent period of sustained improved results to many problems that have been the focus of research for years. The students will perform hands-on research on novel problems, conduct experiments using Transformer-based Deep Learning architectures and Quantum Computing methods, and communicate their results through written papers and presentations. Example projects will involve controlling generation of large language models; defending language models against attacks; and the use of Quantum Computing in solving tasks such as part-of-speech tagging and named entity recognition.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.