7 citations · 8 across the 3 of their papers we have counts for
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cs.CL2025
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…
cs.CL2024★ 1 cited
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…
cs.CL2023★ 7 cited
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…