Papers by Haifeng Qian
ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)
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Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang
| Challenge: | Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area. |
| Approach: | They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. |
| Outcome: | The proposed model performs better on human annotators and on SOTA models with human annnotators. |
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation (2025.naacl-long)
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Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Recent code completion models focus on local file contexts, but do not fully capture the complexities of real-world software development. |
| Approach: | They propose a version-specific code-completion task across eight libraries as they evolve over the years and an in-depth analysis of two widely used public libraries: PyTorch and Matplotlib. |
| Outcome: | The proposed model improves performance with public libraries, compared with existing models. |
TransBench: Breaking Barriers for Transferable Graphical User Interface Agents in Dynamic Digital Environments (2025.findings-acl)
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Yuheng Lu, Qian Yu, Hongru Wang, Zeming Liu, Wei Su, Yanping Liu, Yuhang Guo, Maocheng Liang, Yunhong Wang, Haifeng Wang
| Challenge: | Existing GUI agents struggle to adapt to dynamic and interconnected nature of real-world digital environments, authors show . |
| Approach: | They propose a benchmark to evaluate the transferability of GUI agents across three key dimensions . transBench includes 15 app categories with diverse functionalities . |
| Outcome: | The proposed benchmark shows that existing GUI agents struggle to adapt to dynamic, interconnected environments. |
BASS: Batched Attention-optimized Speculative Sampling (2024.findings-acl)
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Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras
| Challenge: | Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. |
| Approach: | They propose a batched speculative decoding system that generates sequences at an average speed of 5.8ms per token and a batch size of 8 at a 2.15 speed-up over optimized regular decoding. |
| Outcome: | The proposed system achieves state-of-the-art latency and speed-up over optimized regular decoding. |
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes (2026.findings-acl)
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| Challenge: | Existing home assistants struggle to interpret elliptical commands based on ellipine expressions . current assistants overlook the progressive omission that occurs in human dialogue as context accumulates - limiting their effectiveness in real-world applications . |
| Approach: | They propose a simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes. |
| Outcome: | The proposed dataset shows that existing home assistants struggle to execute user-intended operations based solely on elliptical commands. |
CodeFort: Robust Training for Code Generation Models (2024.findings-emnlp)
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Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
| Challenge: | Existing research efforts to improve code generation models are inadequate . code generation model performance is degraded under small perturbations . |
| Approach: | They propose a framework to improve the robustness of code generation models by generalizing code perturbations to enrich training data and enabling various robust training strategies. |
| Outcome: | The proposed framework increases pass rates and robustness drop rate against code-syntax perturbations. |
SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs (2025.findings-emnlp)
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| Challenge: | Existing approaches fail to fully capture all risks in tool utilization, resulting in financial loss or privacy leaking. |
| Approach: | They propose a framework to assess the safety of LLM tool utilization in a prospective manner, covering malicious user instructions and diverse practical toolsets. |
| Outcome: | The proposed framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization. |