3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2025
Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
Guanlin Li, Kangjie Chen, Shangwei Guo +6
Large language models (LLMs) have emerged as powerful tools for addressing a wide range of general inquiries and tasks. Despite this, fine-tuning aligned LLMs on smaller, domain-sp…
cs.LG2024★ 3 cited
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing
Xiao-Yang Liu, Jie Zhang, Guoxuan Wang +2
Large language models (LLMs) are computationally intensive. The computation workload and the memory footprint grow quadratically with the dimension (layer width). Most of LLMs' par…
physics.soc-ph2016★ 1 cited
Navigation by anomalous random walks on complex networks
Tongfeng Weng, Jie Zhang, Moein Khajehnejad +3
Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, tradit…