73 citations · 126 across the 12 of their papers we have counts for
13 papers
Fixing Large Language Models' Specification Misunderstanding for Better Code Generation
Zhao Tian, Junjie Chen, Xiangyu Zhang
Code generation is to automatically generate source code conforming to a given programming specification, which has received extensive attention especially with the development of…
Bias Testing and Mitigation in LLM-based Code Generation
Dong Huang, Jie M. Zhang, Qingwen Bu +3
As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as tho…
On-the-fly Improving Performance of Deep Code Models via Input Denoising
Zhao Tian, Junjie Chen, Xiangyu Zhang
Deep learning has been widely adopted to tackle various code-based tasks by building deep code models based on a large amount of code snippets. While these deep code models have ac…
Bayesian Calibrated Click-Through Auction
Junjie Chen, Minming Li, Haifeng Xu +1
We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payme…
Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
Jingxi Li, Xurong Li, Nezih T. Yardimci +6
Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. Ho…
Code Difference Guided Adversarial Example Generation for Deep Code Models
Zhao Tian, Junjie Chen, Zhi Jin
Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics con…