Diao, Shizhe

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., & Zimmermann, R. (2024) · Proceedings of the ACM Web Conference 2024

Time Series Analysis and ForecastingForecasting Techniques and Applications

Cited by 115

R-Tuning: Instructing Large Language Models to Say ‘I Don’t Know’

Zhang, H., Diao, S., Lin, Y., Fung, Y., Lian, Q., Wang, X., Chen, Y., Ji, H., & Zhang, T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 28

Small Language Models are the Future of Agentic AI

Belcak, P., Heinrich, G., Diao, S., Fu, Y., Dong, X., Muralidharan, S., Lin, Y. C., & Molchanov, P. (2025) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 18

Mitigating the Alignment Tax of RLHF

Lin, Y., Lin, H., Xiong, W., Diao, S., Liu, J., Zhang, J., Pan, R., Wang, H., Hu, W., Zhang, H., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 10

Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data

Shum, K., Diao, S., & Zhang, T. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3