Tang, Ruixiang

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., & Hu, X. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 644

Harnessing the power of llms in practice: a survey on chatGPT and beyond

Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024) · ACM Transactions on Knowledge Discovery from Data

Topic ModelingNatural Language Processing Techniques

Cited by 644

The Science of Detecting LLM-Generated Text

Tang, R., Chuang, Y.-N., & Hu, X. (2024) · Communications of the ACM

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 125

Mitigating Gender Bias in Captioning Systems

Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021

Multimodal Machine Learning ApplicationsEthics and Social Impacts of AI

Cited by 50

SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization

Chuang, Y.-N., Tang, R., Jiang, X., & Hu, X. (2024) · Journal of Biomedical Informatics

Machine Learning in HealthcareTopic Modeling

Cited by 46

Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

Tang, R., Kong, D., Huang, L., & Xue, H. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 31