765 citations · 1k across the 25 of their papers we have counts for
6 papers · 1 filter
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…
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Tianyang Zhong, Zhenyuan Yang, Zhengliang Liu +11
Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face…
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Tianyang Zhong, Zhengliang Liu, Yi Pan +74
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, includi…
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…
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
Yiheng Liu, Tianle Han, Siyuan Ma +15
This paper presents a comprehensive survey of ChatGPT-related (GPT-3.5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective…