Papers by Yuqun Zhang
LLM4Decompile: Decompiling Binary Code with Large Language Models (2024.emnlp-main)
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| Challenge: | Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. |
| Approach: | They propose an open-source LLM series trained to decompile binary code . they optimize the LLM training process and introduce the Llm4Decompile-End models . |
| Outcome: | The proposed models outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks by over 100% in terms of re-executability rate. |
OASIS: Order-Augmented Strategy for Improved Code Search (2025.acl-long)
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Gao Zuchen, Zizheng Zhan, Xianming Li, Erxin Yu, Haotian Zhang, Chenbin Chenbin, Yuqun Zhang, Jing Li
| Challenge: | Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications. |
| Approach: | They propose an order-augmented strategy for improved code search that leverages order-based similarity labels to capture subtle differences in similarity among negative pairs. |
| Outcome: | The proposed model outperforms state-of-the-art models focusing on major positive-negative differences. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |