22 citations · 24 across the 5 of their papers we have counts for
5 papers
DracoGPT: Extracting Visualization Design Preferences from Large Language Models
Huichen Will Wang, Mitchell Gordon, Leilani Battle +1
Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might pro…
Localizing Paragraph Memorization in Language Models
Niklas Stoehr, Mitchell Gordon, Chiyuan Zhang +1
Can we localize the weights and mechanisms used by a language model to memorize and recite entire paragraphs of its training data? In this paper, we show that while memorization is…
Distill, Adapt, Distill: Training Small, In-Domain Models for Neural Machine Translation
Mitchell A. Gordon, Kevin Duh
We explore best practices for training small, memory efficient machine translation models with sequence-level knowledge distillation in the domain adaptation setting. While both do…
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
Mitchell A. Gordon, Kevin Duh, Nicholas Andrews
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models…
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation
Mitchell A. Gordon, Kevin Duh
Sequence-level knowledge distillation (SLKD) is a model compression technique that leverages large, accurate teacher models to train smaller, under-parameterized student models. Wh…