An example preprint / working paper
April 7, 2019·
·
1 min read
Xiangyu Zhou
Image credit: UnsplashAbstract
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.
Type
This work is driven by the results in my previous paper on LLMs.
Note
Create your slides in Markdown - click the Slides button to check out the example.
Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.
Authors
Xiangyu Zhou
(he/him)
Graduate Research Assistant
Hi there! 👋 I’m a Ph.D. candidate in Computer Science at Wayne State University, advised by Prof. Dongxiao Zhu, where I spend most of my time studying how to make large language models more trustworthy, robust, and safe. My research sits at the intersection of trustworthy AI, large language model safety, and reasoning, with a focus on understanding how modern models can be manipulated, misaligned, or made to forget in more precise ways.
Since joining the Trustworthy AI Lab, I have been working on problems such as jailbreak vulnerabilities, adversarial in-context learning, safety alignment, and LLM unlearning. My work explores both the weaknesses of frontier language and reasoning models and practical ways to improve their reliability under real-world conditions.
More recently, I have been studying how reasoning traces and conversational context can steer model behavior, as well as how to align models more effectively without hurting their general usefulness. I am also interested in targeted unlearning: removing unwanted or sensitive information from models while keeping useful knowledge intact. At a broader level, I care about building AI systems that are not only capable, but also dependable and responsible. My long-term goal is to help bridge cutting-edge language model research with safer deployment in high-impact settings.
If you’re interested in trustworthy AI, language model safety, robustness, or reasoning, let’s connect! 🚀
Since joining the Trustworthy AI Lab, I have been working on problems such as jailbreak vulnerabilities, adversarial in-context learning, safety alignment, and LLM unlearning. My work explores both the weaknesses of frontier language and reasoning models and practical ways to improve their reliability under real-world conditions.
More recently, I have been studying how reasoning traces and conversational context can steer model behavior, as well as how to align models more effectively without hurting their general usefulness. I am also interested in targeted unlearning: removing unwanted or sensitive information from models while keeping useful knowledge intact. At a broader level, I care about building AI systems that are not only capable, but also dependable and responsible. My long-term goal is to help bridge cutting-edge language model research with safer deployment in high-impact settings.
If you’re interested in trustworthy AI, language model safety, robustness, or reasoning, let’s connect! 🚀