37 citations · 49 across the 6 of their papers we have counts for
7 papers
Cross-Tokenizer Likelihood Scoring Algorithms for Language Model Distillation
Buu Phan, Ashish Khisti, Karen Ullrich
Computing next-token likelihood ratios between two language models (LMs) is a standard task in training paradigms such as knowledge distillation. Since this requires both models to…
DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models
Revant Teotia, Candace Ross, Karen Ullrich +4
Recent advances in text-to-image (T2I) models have achieved impressive quality and consistency. However, this has come at the cost of representation diversity. While automatic eval…
EvalGIM: A Library for Evaluating Generative Image Models
Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat +14
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound…
Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
Buu Phan, Brandon Amos, Itai Gat +3
Tokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studi…
Understanding and Mitigating Tokenization Bias in Language Models
Buu Phan, Marton Havasi, Matthew Muckley +1
State-of-the-art language models are autoregressive and operate on subword units known as tokens. Specifically, one must encode the conditioning string into a list of tokens before…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…
Image Compression with Product Quantized Masked Image Modeling
Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich +3
Recent neural compression methods have been based on the popular hyperprior framework. It relies on Scalar Quantization and offers a very strong compression performance. This contr…