4 papers · 1 filter
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Tunyu Zhang, Xinxi Zhang, Ligong Han +9
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…
Hopscotch: Discovering and Skipping Redundancies in Language Models
Mustafa Eyceoz, Nikhil Shivakumar Nayak, Hao Wang +2
Modern causal language models stack many attention blocks to improve performance, but not all blocks are necessary for every task. We propose Hopscotch, a simple yet effective meth…
Activation-Informed Merging of Large Language Models
Amin Heyrani Nobari, Kaveh Alim, Ali ArjomandBigdeli +3
Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance ac…
Dr. SoW: Density Ratio of Strong-over-weak LLMs for Reducing the Cost of Human Annotation in Preference Tuning
Guangxuan Xu, Kai Xu, Shivchander Sudalairaj +2
Preference tuning relies on high-quality human preference data, which is often expensive and time-consuming to gather. In this paper, we introduce Dr.SoW (Density Ratio of Strong o…