Quantitative geometry scores across four input types show semantic richness improves LLM-generated CAD fidelity, with code-based prompts reaching perfect scores only after human code edits.
Analysis of ChatGPT on Source Code
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abstract
This paper explores the use of Large Language Models (LLMs) and in particular ChatGPT in programming, source code analysis, and code generation. LLMs and ChatGPT are built using machine learning and artificial intelligence techniques, and they offer several benefits to developers and programmers. While these models can save time and provide highly accurate results, they are not yet advanced enough to replace human programmers entirely. The paper investigates the potential applications of LLMs and ChatGPT in various areas, such as code creation, code documentation, bug detection, refactoring, and more. The paper also suggests that the usage of LLMs and ChatGPT is expected to increase in the future as they offer unparalleled benefits to the programming community.
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Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models
Quantitative geometry scores across four input types show semantic richness improves LLM-generated CAD fidelity, with code-based prompts reaching perfect scores only after human code edits.