80 citations · 192 across the 6 of their papers we have counts for
6 papers
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…
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…
The Science of Detecting LLM-Generated Texts
Ruixiang Tang, Yu-Neng Chuang, Xia Hu
The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by…
Fairness via Representation Neutralization
Mengnan Du, Subhabrata Mukherjee, Guanchu Wang +3
Existing bias mitigation methods for DNN models primarily work on learning debiased encoders. This process not only requires a lot of instance-level annotations for sensitive attri…