1 citations · 1 across the 9 of their papers we have counts for
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Execution-Anchored Hallucination Calibration Reranking for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, yet their performance degrades significantly on low-resource Hardware Description Languag…
From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit…
Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysis
Naizhu Jin, Zhong Li, Guang Yang +2
Large language models (LLMs) achieve strong performance on code generation, but the mechanisms by which Chain-of-Thought (CoT) prompting helps remain unclear. We present a systemat…
CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation
Guang Yang, Yu Zhou, Xiang Chen +5
Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable…
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang +6
Large Language Models (LLMs) and other automated techniques have been increasingly used to support software developers by generating software artifacts such as code snippets, patch…