21 citations · 45 across the 7 of their papers we have counts for
6 papers · 1 filter
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
An Liu, Zonghan Yang, Zhenhe Zhang +6
While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-sp…
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models
Qinhong Zhou, Zonghan Yang, Peng Li +1
Conventional knowledge distillation (KD) methods require access to the internal information of teachers, e.g., logits. However, such information may not always be accessible for la…
On Robust Prefix-Tuning for Text Classification
Zonghan Yang, Yang Liu
Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained model…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
Rui Jiao, Zonghan Yang, Maosong Sun +1
While synthetic bilingual corpora have demonstrated their effectiveness in low-resource neural machine translation (NMT), adding more synthetic data often deteriorates translation…
Neural Machine Translation: A Review of Methods, Resources, and Tools
Zhixing Tan, Shuo Wang, Zonghan Yang +4
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machi…