5 citations · 27 across the 26 of their papers we have counts for
4 papers · 1 filter
Merging Experts into One: Improving Computational Efficiency of Mixture of Experts
Shwai He, Run-Ze Fan, Liang Ding +3
Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture…
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +4
Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the…
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model
Hongtai Jing, Zhengtao Gao, Sheng Xu +7
In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limi…
MerA: Merging Pretrained Adapters For Few-Shot Learning
Shwai He, Run-Ze Fan, Liang Ding +3
Adapter tuning, which updates only a few parameters, has become a mainstream method for fine-tuning pretrained language models to downstream tasks. However, it often yields subpar…