1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.CL2025
Traversal Verification for Speculative Tree Decoding
Yepeng Weng, Qiao Hu, Xujie Chen +5
Speculative decoding is a promising approach for accelerating large language models. The primary idea is to use a lightweight draft model to speculate the output of the target mode…
cs.CL2025
CORAL: Learning Consistent Representations across Multi-step Training with Lighter Speculative Drafter
Yepeng Weng, Dianwen Mei, Huishi Qiu +4
Speculative decoding is a powerful technique that accelerates Large Language Model (LLM) inference by leveraging a lightweight speculative draft model. However, existing designs su…
cs.CL2023★ 1 cited
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models
Yimin Jing, Renren Jin, Jiahao Hu +4
The effective assessment of the instruction-following ability of large language models (LLMs) is of paramount importance. A model that cannot adhere to human instructions might be…