Qiusi Zhan | 詹秋思
I am a 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 2025 summer.
Email  / 
CV  / 
Google Scholar  / 
Github
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Research
I have a broad research interest in natural language processing, currently
centered on making large language models (LLMs) and LLM agents more robust and trustworthy. I also have experience in the fields of event extraction and dialog systems.
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INJECAGENT: Benchmarking Indirect Prompt Injections in
Tool-Integrated Large Language Model Agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, Daniel Kang
ACL Findings, 2024
We benchmark IPI attacks in tool-integrated LLM agents and show that most of the agents are vulnerable to such attacks.
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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.
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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.
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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.
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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.
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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.
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University of Illinois Urbana-Champaign, IL
2023.08 - Present
Ph.D. Student in Computer Science
Advisor: Prof. Daniel Kang
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University of Illinois Urbana-Champaign, IL
2021.08 - 2022.12
Master in Electronical and Computer Engineering
Advisor: Prof. Heng Ji
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Peking University, China
2017.09 - 2021.07
B.S. in Computer Science
Advisor: Prof. Sujian Li
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University of California Santa Barbara, CA
2020.07 - 2020.09
Visiting Research Student
Advisor: Prof. Xifeng Yan
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Microsoft, WA
2024.05 - 2024.08
Applied Scientist Intern
Mentor: Yu Hu
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JD.COM Silicon Valley Research Center, CA
2022.01 - 2022.05
Research Scientist Intern
Mentor: Lingfei Wu
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ByteDance, China
2021.04 - 2021.07
Natural Language Processing Engineer Intern
Mentor: Bo Zhao
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Last updated: Oct. 9, 2023
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