18 citations · 30 across the 3 of their papers we have counts for
3 papers
AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews
Keith Tyser, Ben Segev, Gaston Longhitano +9
Automatic reviewing helps handle a large volume of papers, provides early feedback and quality control, reduces bias, and allows the analysis of trends. We evaluate the alignment o…
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models
Sarah J. Zhang, Samuel Florin, Ariel N. Lee +12
We curate a comprehensive dataset of 4,550 questions and solutions from problem sets, midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and Compu…
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams
Iddo Drori, Sarah J. Zhang, Reece Shuttleworth +13
A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large…