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Yuheng Huang

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Articles

Computer Science > Software Engineering

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By Yinxing Xue, Lei Ma, Yuheng Huang
| arXiv
As Code Large Language Models (LLMs) become central to modern software engineering, their inherent stochasticity poses significant real-world risks, where even minor errors can lead to severe functional, security, or safety consequences. Reliable automation, therefore, demands the ability to distinguish between confident, well-supported predictions and stochastic guessing.
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Editorial for “Use of Blood Oxygenation Level‐Dependent MRI to Predict Clinical Outcomes After Endovascular Revascularization in Peripheral Artery Disease”

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By Xinheng Zhang, Yuheng Huang, Hsin-Jung Yang
| Wiley Online Library Verified
EDITORIAL Editorial for “Use of Blood Oxygenation Level-Dependent MRI to Predict Clinical Outcomes After Endovascular Revascularization in Peripheral Artery Disease” Use of Blood Oxygenation Level-Dependent MRI to Predict Clinical Outcomes After Endovascular Revascularization in Peripheral Artery Disease Shiteng Suo, Hui Tang, Mengqiu Cao, Qihong Ni, Jianxun Qu, Jianrong Xu, Bo Li, Lan Zhang, Qing Lu, Yan Zhou, Journal of Magnetic Resonance Imaging First Published online: July 9, 2025 Xinheng...
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Evaluation and Improvement of Test Selection for Large Language Models

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By Lili Quan, Jin Wen, Qiang Hu, Yuheng Huang
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. References 1, , , et al., “ Large Language Models Are Human-Level Prompt Engineers,” in The Eleventh International Conference on Learning Representations (2022). 2, , , , and , “ Sentiment Analysis in the Era of Large Language Models: A Reality Check,” in Findings of the Association for Computational Linguistics (NAACL, 2024), 3881–3906.
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