Hao Mi   米昊

I am a M.S. student at the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS), working in the Media Synthesis and Forensics Lab under the supervision of Prof. Juan Cao and Prof. Qiang Sheng. Prior to this, I received my Bachelor's degree in Computer Science and Technology from Xi'an Jiaotong University (XJTU).

My research interests focus on the misinformation and factuality in the era of large language models, which is a critical issue for constructing the reliable and trustworthy LLM-based systems. Currently, I am mainly working on hallucination detection in LLMs.

Feel free to reach out for research collaborations on LLM factuality!

Contact: mihao24s[AT]ict[dot]ac[dot]cn

profile photo

📍 Beijing, China

Publications

ACL 2026 ACL'26 Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang, and Juan Cao
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics
Paper (TBA)
TL;DR: We leverage the inherent logical consistency between LLM responses and their self-judgments as a bridging signal to improve hallucination detection.
PhantomHunter Preprint PhantomHunter: Detecting Unseen Privately-Tuned LLM-Generated Text via Family-Aware Learning
Yuhui Shi, Yehan Yang, Qiang Sheng, Beizhe Hu, Hao Mi, Chaoxi Xu, and Juan Cao
Preprint / Media Coverage: Unite.AI
TL;DR: We introduce a family-aware learning framework that captures shared traits across base and privately tuned LLMs to robustly detect unseen LLM-generated text.
IJCAI 2024 IJCAI'24 Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, and Danding Wang
Proceedings of the 33rd International Joint Conference on Artificial Intelligence
Preprint / Paper / GitHub Repo / Chinese Blog
TL;DR: To detect and attribute text generated by black-box LMs, we estimate the generation probabilities of representative tokens using a white-box proxy LM, yielding a discriminative feature.

Education

2024 – Present
M.S. student in Computer Science and Technology
University of Chinese Academy of Sciences (Cultivation Unit: Institute of Computing Technology, CAS)
2020 – 2024
B.E. in Computer Science and Technology
Qian Xuesen Honors College, Xi'an Jiaotong University

Honors and Awards

  • 2026   Merit Student, University of Chinese Academy of Sciences
  • 2023   Undergraduate Scholarship, University of Chinese Academy of Sciences
  • 2021, 2022, 2023   Academic Scholarship, Xi'an Jiaotong University

Miscellaneous

Just for fun: photography📷, biking🚴, cooking🍳, puzzle games💭, sneakers👟...

Website template from Jon Barron and Qiang Sheng.