Qiusi Zhan | 詹秋思

I am a first-year Ph.D. student at the Univerity of Illinois Urbana Champaign (UIUC), advised by Prof. Daniel Kang. Previously, I obtained my master's degree from UIUC, advised by Prof. Heng Ji, and completed my bachelor's degree from Peking University, advised by Prof. Sujian Li.

I am looking for research internship opportunities for 2024 summer.

Email  /  CV  /  Google Scholar  /  Github

profile photo
Research

I have a broad research interest in Natural Language Processing, currently centered on making large language models more robust and trustworthy. I also have experience in the fields of event extraction and dialog systems.

INJECAGENT: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang
arXiv preprint, 2024

We benchmark IPI attacks in tool-integrated LLM agents and show that most of the agents are vulnerable to such attacks.

Removing RLHF Protections in GPT-4 via Fine-Tuning
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, Daniel Kang
NAACL, 2024

We demonstrate that fine-tuning GPT-4 with just 340 examples can subvert its RLHF protections, underscoring the critical need for enhanced security measures.

GLEN: General-Purpose Event Detection for Thousands of Types
Qiusi Zhan*, Sha Li*, Kathryn Conger , Martha Palmer , Heng Ji , Jiawei Han
EMNLP, 2023

We build a wide‑coverage event detection dataset, with 205K event mentions covering 3,465 types and develop a novel multi‑stage event detection model.

User Simulator Assisted Open-ended Conversational Recommendation System
Qiusi Zhan, Xiaojie Guo, Heng Ji, Lingfei Wu
ACL Workshop, 2023

We fine-tune pre-trained CRS based on its interaction with a designed User Simulator using reinforcement learning to largely improve its recommendation performance.

EA2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
Qi Zeng*, Qiusi Zhan*, Heng Ji
NAACL Findings, 2022

We improve the argument consistency of multiple events in documents by making the model aware of event relations.

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Dawei Zhu, Qiusi Zhan, Zhejian Zhou, Yifan Song, Jiebin Zhang, Sujian Li
COLING, 2022

We benchmark the detection of Chinese Figures of Speech.

Experience
University of Illinois Urbana-Champaign, IL
2023.08 - Present

Ph.D. Student in Computer Science
Advisor: Prof. Daniel Kang
University of Illinois Urbana-Champaign, IL
2021.08 - 2022.12

Master in Electronical and Computer Engineering
Advisor: Prof. Heng Ji
Peking University, China
2017.09 - 2021.07

B.S. in Computer Science
Advisor: Prof. Sujian Li
University of California Santa Barbara, CA
2020.07 - 2020.09

Visiting Research Student
Advisor: Prof. Xifeng Yan
Internship
JD.COM Silicon Valley Research Center, CA
2022.01 - 2022.05

Research Scientist Intern
Mentor: Dr. Lingfei Wu
ByteDance, China
2021.04 - 2021.07

Natural Language Processing Engineer Intern
Mentor: Dr. Bo Zhao

This website is based on a template created by Jon Barron.
Last updated: Oct. 9, 2023