Papers by Mingzhe Du
CodeArena: A Collective Evaluation Platform for LLM Code Generation (2025.acl-demo)
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Mingzhe Du, Anh Tuan Luu, Bin Ji, Xiaobao Wu, Yuhao Qing, Dong Huang, Terry Yue Zhuo, Qian Liu, See-Kiong Ng
| Challenge: | Large Language Models (LLMs) have reshaped code generation, but persistent challenges impede accurate assessment. |
| Approach: | They propose an online evaluation framework tailored for large language models to assess their coding capabilities. |
| Outcome: | a new evaluation framework for large language models (LLMs) provides unbiased, unbiased evaluations and open access to solutions and test cases. |
SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)
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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo
| Challenge: | Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone . |
| Approach: | They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis. |
On Assigning Product and Software Codes to Customer Service Requests with Large Language Models (2025.emnlp-industry)
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| Challenge: | In a technology company, quality of customer service is a crucial asset. |
| Approach: | They propose to use Large Language Models to assign product names and software version labels to customer Service Requests (SRs) they frame assignment as multiple-choice question answering task instead of conventional prompts . |
| Outcome: | The proposed model can identify product names and software versions when they are mentioned with over 90% accuracy while cutting LLM costs by 40-60% on average. |
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025.acl-long)
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Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang
| Challenge: | Existing studies solve this challenge by updating benchmarks with newly collected data, but they fail to guarantee contamination-free evaluation as the newly collected knowledge may contain pre-existing knowledge. |
| Approach: | They propose an automated anti-leakage benchmarking framework that builds and updates benchmarks without human labor instead of using newly collected data. |
| Outcome: | The proposed framework significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. |
Pro-QuEST: A Prompt-chain based Quiz Engine for testing Specialized Technical Product Knowledge (2026.eacl-demo)
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| Challenge: | Specialized benchmarks can be leveraged to create quizzes that can effectively train engineering and marketing personnel on novel product offerings in a continually growing Cisco product space. |
| Approach: | They propose to generate multiple-choice questions using domain-specific prompts using a set of professional certification textbooks and a range of latest open-source and proprietary LLMs. |
| Outcome: | The proposed quiz engine generates multiple-choice questions using domain-specific prompts and a range of latest open-source, and proprietary LLMs. |