7 citations · 8 across the 5 of their papers we have counts for
4 papers · 1 filter
Beyond Steering Vector: Flow-based Activation Steering for Inference-Time Intervention
Zehao Jin, Ruixuan Deng, Junran Wang +2
Activation steering has emerged as a promising alternative for controlling language-model behavior at inference time by modifying intermediate representations while keeping model p…
EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Yuhui Li, Fangyun Wei, Chao Zhang +1
The sequential nature of modern LLMs makes them expensive and slow, and speculative sampling has proven to be an effective solution to this problem. Methods like EAGLE perform auto…
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees
Yuhui Li, Fangyun Wei, Chao Zhang +1
Inference with modern Large Language Models (LLMs) is expensive and time-consuming, and speculative sampling has proven to be an effective solution. Most speculative sampling metho…
RAIN: Your Language Models Can Align Themselves without Finetuning
Yuhui Li, Fangyun Wei, Jinjing Zhao +2
Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained m…