Graduate Student, University of Illinois at Urbana-Champaign
Undergraduate Student, Tsinghua UniversityHi, there! I'm Zhe Wang, a first year M.S. student at University of Illinois at Urbana-Champaign, majoring in Computer Science. I am advised by Prof. Lingming Zhang.
Before coming to UIUC, I completed my undergraduate study at Tsinghua University, majoring in Mathematics and Physics.
Research Interests
My research interest lies in the intersection of Artificial Intelligence and Software Engineering. More specifically:
- LLMs for Code: to develop LLMs to solve software engineering tasks through post-training via synthetic data
- Trustworthy LLMs: to enhance trustworthiness, resilience and reliability of helpful-only LLMs against vulnerable code and malicious cyberactivity attacks
- LLM Applications: to empower LLMs with reasoning, planning and collaboration capabilities through alignment training and agent-based systems
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University of Illinois at Urbana-ChampaignM.S. in Computer ScienceAug. 2024 - Present
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Tsinghua UniversityB.S. in Mathematics and PhysicsSep. 2020 - Jul. 2024
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Shanghai High SchoolHigh SchoolSep. 2017 - Jun. 2020
Selected Publications (view all )
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
Kunlun Zhu†, Hongyi Du†, Zhaochen Hong†, Xiaocheng Yang†, Shuyi Guo†, Zhe Wang†, Zhenhailong Wang, Cheng Qian, Xiangru Tang, Heng Ji, Jiaxuan You († core contributors)
ACL 2025
In this paper, we introduce MultiAgentBench, a comprehensive benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. Our framework measures not only task completion but also the quality of collaboration and competition using novel, milestone-based key performance indicators.
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
Kunlun Zhu†, Hongyi Du†, Zhaochen Hong†, Xiaocheng Yang†, Shuyi Guo†, Zhe Wang†, Zhenhailong Wang, Cheng Qian, Xiangru Tang, Heng Ji, Jiaxuan You († core contributors)
ACL 2025
In this paper, we introduce MultiAgentBench, a comprehensive benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. Our framework measures not only task completion but also the quality of collaboration and competition using novel, milestone-based key performance indicators.
Magicoder: Empowering Code Generation with OSS-Instruct
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang
ICML 2024
In this paper, we introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while having no more than 7B parameters.
Magicoder: Empowering Code Generation with OSS-Instruct
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang
ICML 2024
In this paper, we introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while having no more than 7B parameters.