12/2024
|
Invited to serve as a PC member for WWW 2025.
|
07/2024
|
One co-authored papers got accepted by CIKM 2024.
|
04/2024
|
One co-authored papers got accepted by IJCAI 2024.
|
02/2024
|
Invited to serve as a Reviewer for ACL Rolling Review.
|
12/2023
|
One first-authored paper got accepted by AAAI 2024.
|
05/2023
|
One first-authored paper got accepted by ACL 2023.
|
|
Preprint
|
Understanding News Creation Intents: Frame, Dataset, and Method
Zhengjia Wang, Danding Wang, Qiang Sheng, Juan Cao, Silong Su, Yifan Sun, Beizhe Hu, and Siyuan Ma
arXiv:2312.16490
|
2024
|
[CIKM 2024] Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models
Qiong Nan, Qiang Sheng, Juan Cao,Beizhe Hu, Danding Wang, and Jintao Li
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
Preprint /
Paper /
GitHub Repo /
Chinese Blog
TL;DR: We prompt LLMs to role-play social media users to obtain generated comments for enhancing fake news detection.
|
[IJCAI 2024] 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 /
GitHub Repo /
Chinese Blog
TL;DR: To detect and attribute text generated by black-box LMs, we estimate their generation probabilities of representative words guided by a white-box proxy LM to obtain a strong feature.
|
[AAAI 2024] Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi
Proceedings of the 38th AAAI Conference on Artificial Intelligence
Preprint /
Paper /
GitHub Repo /
English Video & Slides
TL;DR: Large LMs generally underperform fine-tuned Small LMs for fake news detection, but they can be good advisors by providing rationales.
|
2023
|
[ACL 2023] Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
Beizhe Hu, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Zhengjia Wang, and Zhiwei Jin
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
Preprint /
Paper /
Poster /
Slides
TL;DR: We propose to address the temporal shift issue in real-world fake news detection systems via forecasting topic-level trends and accordingly adjusting the detector update strategy.
|
|
2024
|
Pacemaker to Merit Student of UCAS, UCAS
|
2023
|
National Scholarship, Ministry of Education of China
|
2023
|
First-Class Academic Scholarship, University of Chinese Academy of Sciences
|
2023
|
Merit Student, University of Chinese Academy of Sciences
|
2019, 2020
|
First-Class Academic Scholarship, Xidian University
|
|