65 citations · 149 across the 12 of their papers we have counts for
4 papers · 1 filter
Sparse Low-rank Adaptation of Pre-trained Language Models
Ning Ding, Xingtai Lv, Qiaosen Wang +4
Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoR…
Empowering Private Tutoring by Chaining Large Language Models
Yulin Chen, Ning Ding, Hai-Tao Zheng +3
Artificial intelligence has been applied in various aspects of online education to facilitate teaching and learning. However, few approaches has been made toward a complete AI-powe…
Exploring Lottery Prompts for Pre-trained Language Models
Yulin Chen, Ning Ding, Xiaobin Wang +4
Consistently scaling pre-trained language models (PLMs) imposes substantial burdens on model adaptation, necessitating more efficient alternatives to conventional fine-tuning. Give…
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Ning Ding, Yulin Chen, Bokai Xu +6
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such dat…