3 citations · 5 across the 3 of their papers we have counts for
5 papers
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu +10
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource lan…
IAPT: Instruction-Aware Prompt Tuning for Large Language Models
Wei Zhu, Aaron Xuxiang Tian, Congrui Yin +3
Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guara…
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
Zequan Liu, Jiawen Lyn, Wei Zhu +2
Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated comme…
Text2MDT: Extracting Medical Decision Trees from Medical Texts
Wei Zhu, Wenfeng Li, Xing Tian +6
Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support systems. However, the current MDT c…
UltraFeedback: Boosting Language Models with Scaled AI Feedback
Ganqu Cui, Lifan Yuan, Ning Ding +9
Learning from human feedback has become a pivot technique in aligning large language models (LLMs) with human preferences. However, acquiring vast and premium human feedback is bot…