908 citations · 2.4k across the 149 of their papers we have counts for
3 papers · 2 filters
Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks
Sergey Pankratov, Dan Alistarh
Speculative generation has emerged as a promising technique to accelerate inference in large language models (LLMs) by leveraging parallelism to verify multiple draft tokens simult…
Efficient Data Selection at Scale via Influence Distillation
Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh +1
Effective data selection is critical for efficient training of modern Large Language Models (LLMs). This paper introduces Influence Distillation, a novel, mathematically-justified…
Position: It's Time to Act on the Risk of Efficient Personalized Text Generation
Eugenia Iofinova, Andrej Jovanovic, Dan Alistarh
The recent surge in high-quality open-source Generative AI text models (colloquially: LLMs), as well as efficient finetuning techniques, have opened the possibility of creating hig…