Papers by Lingming Zhang
UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging (2025.emnlp-main)
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Cheryl Lee, Chunqiu Steven Xia, Longji Yang, Jen-tse Huang, Zhouruixing Zhu, Lingming Zhang, Michael R. Lyu
| Challenge: | Existing LLMs focus on isolated steps and struggle with complex bugs. |
| Approach: | They propose a framework for unified debugging through multi-agent synergy . it mimics the entire cognitive processes of developers with each agent specialized as a particular component of this process . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on repo-level benchmarks. |
\mathcal XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts (2024.acl-long)
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| Challenge: | Existing studies focus on the data perspectives of instruction tuning, leaving room for exploring advanced training schemes. |
| Approach: | They argue that prior works overlook the possibility of improving code instruction tuning by advancing existing training schemes. |
| Outcome: | The proposed model is dense because all parameters are activated to predict the next token (assuming it is a decoder-only LLM). |
TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)
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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma
| Challenge: | Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases. |
| Approach: | They propose a test case generation benchmark that uses large language models to generate unit test cases. |
| Outcome: | The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths. |