2 citations · 5 across the 14 of their papers we have counts for
4 papers · 1 filter
Beware of Calibration Data for Pruning Large Language Models
Yixin Ji, Yang Xiang, Juntao Li +5
As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency. P…
Demonstration Augmentation for Zero-shot In-context Learning
Yi Su, Yunpeng Tai, Yixin Ji +3
Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations with…
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Dan Qiao, Yi Su, Pinzheng Wang +18
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
Yixin Ji, Yang Xiang, Juntao Li +6
In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating…