12 citations · 16 across the 9 of their papers we have counts for
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cs.CL2025
Understanding the Ability of LLMs to Handle Character-Level Perturbation
Anyuan Zhuo, Xuefei Ning, Ningyuan Li +3
This work investigates the resilience of contemporary large language models (LLMs) against frequent character-level perturbations. We examine three types of character-level perturb…
cs.CL2024★ 2 cited
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Xuefei Ning, Zifu Wang, Shiyao Li +7
Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also t…
cs.CL2023★ 12 cited
Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
Xuefei Ning, Zinan Lin, Zixuan Zhou +3
This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding ap…