3 citations · 3 across the 4 of their papers we have counts for
5 papers
Enhancing Diffusion Language Models with Autoregressive Post-Training Weights
Yiming Qin, Ke Wang, Amel Abdelraheem +2
Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders and parallel decoding. Recen…
Model soups need only one ingredient
Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh +1
Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representatio…
Task Addition and Weight Disentanglement in Closed-Vocabulary Models
Adam Hazimeh, Alessandro Favero, Pascal Frossard
Task arithmetic has recently emerged as a promising method for editing pre-trained \textit{open-vocabulary} models, offering a cost-effective alternative to standard multi-task fin…
Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling
Adam Hazimeh, Ke Wang, Mark Collier +3
Multimedia documents such as slide presentations and posters are designed to be interactive and easy to modify. Yet, they are often distributed in a static raster format, which lim…
Towards Modeling Learner Performance with Large Language Models
Seyed Parsa Neshaei, Richard Lee Davis, Adam Hazimeh +3
Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token seq…