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Ying Hei

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Articles

[2511.18306] Table Comprehension in Building Codes using Vision Language Models and Domain-Specific Fine-Tuning

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By Ying Hei, Mohd Hamza, Ying Hei Chui, Qipei Mei
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2505.04666] Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes

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By Ying Hei, Mohd Hamza, Qipei Mei, Ying Hei Chui
| arXiv
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The Framework and Implementation of Using Large Language Models to Answer Questions about Building Codes and Standards

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By Ying Hei, Talebi Kalaleh
| ascelibrary.org
Practical Applications New and powerful large language models (LLMs), such as ChatGPT, are rapidly changing the way in which professional work is done. However, these state-of-the-art LLMs alone lack the accuracy needed for truthfully answering detailed technical questions, limiting their utility in engineering contexts. Civil engineers often have to manually check designs for compliance with codes and standards, but this task can be difficult and time-consuming, especially for junior engineers.
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