5 citations · 11 across the 5 of their papers we have counts for
6 papers
Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertise
Qimin Yang, Rongsheng Wang, Jiexin Chen +2
Large Language Models (LLMs) have been widely applied in various professional fields. By fine-tuning the models using domain specific question and answer datasets, the professional…
AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
Jiaxi Cui, Wentao Zhang, Jing Tang +6
The pervasive deployment of Large Language Models-LLMs in various sectors often neglects the nuanced requirements of individuals and small organizations, who benefit more from mode…
LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning
Rongsheng Wang, Haoming Chen, Ruizhe Zhou +6
ChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-gene…
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning
Rongsheng Wang, Haoming Chen, Ruizhe Zhou +8
Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhib…
Machine Mindset: An MBTI Exploration of Large Language Models
Jiaxi Cui, Liuzhenghao Lv, Jing Wen +4
We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consis…
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions
Rongsheng Wang, Qi Li, Sihong Xie
General large language models (LLMs) such as ChatGPT have shown remarkable success, but it has also raised concerns among people about the misuse of AI-generated texts. Therefore,…