Platypus: Quick, Cheap, and Powerful Refinement of LLMs

2 code implementations • 14 Aug 2023 • Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz

We present $\textbf{Platypus}$, a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work.

Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing

no code implementations • 30 Jun 2023 • Ariel N. Lee, Sarah Adel Bargal, Janavi Kasera, Stan Sclaroff, Kate Saenko, Nataniel Ruiz

We hypothesize that this power to ignore out-of-context information (which we name $\textit{patch selectivity}$), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion.

Data Augmentation Inductive Bias

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