14 citations · 16 across the 5 of their papers we have counts for
5 papers
FNF: Functional Network Fingerprint for Large Language Models
Yiheng Liu, Junhao Ning, Sichen Xia +8
The development of large language models (LLMs) is costly and has significant commercial value. Consequently, preventing unauthorized appropriation of open-source LLMs and protecti…
Pruning Large Language Models by Identifying and Preserving Functional Networks
Yiheng Liu, Junhao Ning, Sichen Xia +5
Structured pruning is one of the representative techniques for compressing large language models (LLMs) to reduce GPU memory consumption and accelerate inference speed. It offers s…
Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models
Yiheng Liu, Zhengliang Liu, Zihao Wu +10
In recent years, the rapid advancement of large language models (LLMs) in natural language processing has sparked significant interest among researchers to understand their mechani…
Brain-like Functional Organization within Large Language Models
Haiyang Sun, Lin Zhao, Zihao Wu +7
The human brain has long inspired the pursuit of artificial intelligence (AI). Recently, neuroimaging studies provide compelling evidence of alignment between the computational rep…
Understanding LLMs: A Comprehensive Overview from Training to Inference
Yiheng Liu, Hao He, Tianle Han +18
The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cos…