Publications

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Journal Articles


BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement

Published in arXiv, 2024

BlenderLLM, a large language model for CAD tasks, leverages the BlendNet dataset and CADBench benchmarks to achieve superior accuracy and functionality through self-improvement.

Recommended citation: Du, Y., Chen, S., Zan, W., Li, P., Wang, M., Song, D., Li, B., Hu, Y., & Wang, B. (2024). "BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement." arXiv preprint arXiv:2412.14203.
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Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion

Published in arXiv, 2024

AraLLaMA employs progressive vocabulary expansion to improve Arabic LLMs, achieving strong performance on Arabic benchmarks.

Recommended citation: Zhu, J., Huang, H., Lin, Z., Liang, J., Tang, Z., Almubarak, K., Alharthik, A., An, B., He, J., Wu, X., Yu, F., Chen, J., Ma, Z., Du, Y., Zhang, H., Alghamdi, E. A., Zhang, L., Sun, R., Li, H., Wang, B., & Xu, J. (2024). "Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion." arXiv preprint arXiv:2412.12310.
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How did international air transport networks influence the spread of COVID-19? A spatial and temporal modeling perspective

Published in Transportation Research Part C: Emerging Technologies, 2024

This study investigates the spatial and temporal impacts of international air transport networks on the spread of COVID-19 and its variants, offering insights into policy-making for aviation stakeholders.

Recommended citation: Li, C., Yu, L., Mao, J., Cong, W., Pan, Z., Du, Y., & Zhang, L. (2024). "How did international air transport networks influence the spread of COVID-19? A spatial and temporal modeling perspective." Transportation Research Part C: Emerging Technologies, 165, 104730. https://doi.org/10.1016/j.trc.2024.104730
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