hi@zhangchen
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Hello, this is

Zhangchen Xu (徐张晨).

Bio

I am a third-year PhD student at Network Security Lab at the University of Washington, advised by Prof. Radha Poovendran. I’m also a part-time research intern at Microsoft GenAI, working with Dr. Yang Liu. Prior to joining UW, I completed a joint B.E. in Communication Engineering from the University of Electronic Science and Technology of China (UESTC) and the University of Glasgow (UofG). During my undergrad, I was advised by Prof. Lei Zhang.

My email -> zxu9 [a-t] uw [d-o-t] edu. Feel free to reach out if you would like to discuss Synthetic Data, Safety, and Post-training of LLMs, SLMs and VLMs.

Research Interests

I work on Generative AI, with a current focus on the post-training of large language models (LLMs). My current research directions include:

Synthetic Data

I conduct data-centric research focused on enhancing LLMs through post-training with synthetic data.

  • 🦅 Magpie is a family of SOTA synthetic datasets for LLM alignment.
  • 🐦 MagpieLM models are SOTA small language models for chat.
  • 🐱 KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for LLM coding.

In addition, I am interested in distilling capabilities from powerful LLMs to more efficient smaller models. My analysis papers in this area include:

LLM Safety

I investigate emerging security threats in LLMs and explore defense mechanisms. I’m particularly interested in inference-time defenses.

  • SafeDecoding is an inference-time defense against jailbreak attacks.
  • CleanGen mitigates backdoor attacks for generation tasks in inference time.
  • Shield defends against prompt-injection attacks in LLM-integrated Apps using cryptography.

I have also been working on distributed algorithms:

Federated Learning. Work includes ACE (contribution evaluation attack) and Brave.

Distributed Consensus. Work includes Voting Validity, Wireless Distributed Consensus, and Distributed Consensus Network.

Selected Work (see here for full publication list)

KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding

Zhangchen Xu, Yang Liu, Yueqin Yin, Mingyuan Zhou, Radha Poovendran

Arxiv / Website / Huggingface / Code /

Stronger Models are NOT Stronger Teachers for Instruction Tuning

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Bill Yuchen Lin, Radha Poovendran

NAACL 2025 (Main) | Paper

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, Bill Yuchen Lin

ICLR 2025 | Paper / Website / Huggingface / Code / Demo / 新智元

ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates

Fengqing Jiang*, Zhangchen Xu*, Luyao Niu*, Bill Yuchen Lin, Radha Poovendran

AAAI 2025 | Paper / Code

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bo Li, Radha Poovendran

Usenix Security 2024 | Paper / Slides

CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models

Yuetai Li*, Zhangchen Xu*, Fengqing Jiang, Luyao Niu, Dinuka Sahabandu, Bhaskar Ramasubramanian, Radha Poovendran

EMNLP 2024 (Main) | Paper / Code

SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bill Yuchen Lin, Radha Poovendran

ACL 2024 (Main, Oral) | Paper / Code / Poster / Slides

ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs

Fengqing Jiang*, Zhangchen Xu*, Luyao Niu*, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, Radha Poovendran

ACL 2024 (Main) | Paper / Code / Poster