234 citations · 476 across the 20 of their papers we have counts for
6 papers · 1 filter
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Xinlu Zhang, Chenxin Tian, Xianjun Yang +3
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications. However, pr…
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Zhiyuan Hu, Yue Feng, Yang Deng +4
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular…
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection
Zekun Li, Baolin Peng, Pengcheng He +1
Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, becoming increasingly crucial across various applications. However, this capability…
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
Zekun Li, Shiyang Li, Xifeng Yan
Irregularly sampled time series are increasingly prevalent, particularly in medical domains. While various specialized methods have been developed to handle these irregularities, e…
CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue
Xing Cui, Zekun Li, Peipei Li +3
This paper explores interactive facial image editing via dialogue and introduces the ChatEdit benchmark dataset for evaluating image editing and conversation abilities in this cont…
Guiding Large Language Models via Directional Stimulus Prompting
Zekun Li, Baolin Peng, Pengcheng He +3
We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLM…