19 citations · 45 across the 6 of their papers we have counts for
6 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…
Analyzing Nobel Prize Literature with Large Language Models
Zhenyuan Yang, Zhengliang Liu, Jing Zhang +19
This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compa…
A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks
Jiaqi Wang, Hanqi Jiang, Yiheng Liu +21
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (…
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…
Holistic Evaluation of GPT-4V for Biomedical Imaging
Zhengliang Liu, Hanqi Jiang, Tianyang Zhong +47
In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial gener…