Power industry large language model capability evaluation research

Xiang Deng; Xudong Zhang; Shuo Zhang; Houming Jiang; Yuhui Chen

doi:10.1117/12.3051658

18 November 2024 Power industry large language model capability evaluation research

Xiang Deng, Xudong Zhang, Shuo Zhang, Houming Jiang, Yuhui Chen

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033W (2024) https://doi.org/10.1117/12.3051658
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

With the rapid development of artificial intelligence technology, large language models are increasingly applied in the power industry, providing strong support for the intelligent management of power systems. This paper proposes an evaluation system for large language models in the power domain, aiming to evaluate their performance and limitations in solving specific tasks within the power industry. Through testing on the evaluation dataset, we analyzed the performance of large language models in both general domain and the power industry. The experimental results show that domain-adapted power large language model should balance both power industry-specific and general domain capabilities during evaluation. This research has significant theoretical and practical value for the evaluation of large language models in other industries.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiang Deng, Xudong Zhang, Shuo Zhang, Houming Jiang, and Yuhui Chen "Power industry large language model capability evaluation research", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033W (18 November 2024); https://doi.org/10.1117/12.3051658

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