Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

no code implementations • 15 Jun 2023 • Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, Iddo Drori

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 Computer Science (EECS) courses required for obtaining a degree.

Electrical Engineering Few-Shot Learning +3

Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark

no code implementations • 22 Nov 2022 • Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera, Zad Chin, Keith Tyser, Gregory Hunter, Arvind Raghavan, Yann Hicke, Bryan A. Plummer, Ori Kerret, Tonio Buonassisi, Kate Saenko, Armando Solar-Lezama, Iddo Drori

For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly.

Attribute Text-to-Image Generation

From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams

no code implementations • 11 Jun 2022 • Iddo Drori, Sarah J. Zhang, Reece Shuttleworth, Sarah Zhang, Keith Tyser, Zad Chin, Pedro Lantigua, Saisamrit Surbehera, Gregory Hunter, Derek Austin, Leonard Tang, Yann Hicke, Sage Simhon, Sathwik Karnik, Darnell Granberry, Madeleine Udell

We curate a dataset and benchmark of questions from machine learning final exams available online and code for answering these questions and generating new questions.

BIG-bench Machine Learning Few-Shot Learning +4