151 citations · 413 across the 25 of their papers we have counts for
7 papers · 2 filters
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Ruixiang Tang, Gord Lueck, Rodolfo Quispe +3
Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose in…
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Ruixiang Tang, Dehan Kong, Longtao Huang +1
Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). D…
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang +5
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (N…
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang +1
Recent advancements in large language models (LLMs) have led to the development of highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional performance i…
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching
Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang +1
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges s…
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization
Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang +1
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucia…