Method for extracting power emergency plan information based on LLM prompt learning

Zeyu Liu; Yitong Liu; Zehao Zhang; Lei Di; Feng Wei; Yin Wang

doi:10.1117/12.3025216

21 February 2024 Method for extracting power emergency plan information based on LLM prompt learning

Zeyu Liu, Yitong Liu, Zehao Zhang, Lei Di, Feng Wei, Yin Wang

Proceedings Volume 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024); 130800G (2024) https://doi.org/10.1117/12.3025216
Event: International Conference on Frontiers of Applied Optics and Computer Engineering, 2024, Kunming, China

Abstract

The Large Language Model (LLM) as a representative of generative artificial intelligence, demonstrates strong capabilities in natural language comprehension, which was recently put into engineering applications in the field of power emergency. The author proposes a method of extracting information from power emergency plans by leveraging its emergent abilities and prompt learning techniques. By this method, custom-defined contents can be extracted from power emergency plans and linked to the corresponding personnel to generate executable task instructions. The results indicated that this method can accurately extract the custom-defined information from power emergency plans and applys to different LLMs. And the stronger the emergent abilities of the LLM, the more accurate the information extraction is. The method is proofed to effectively assist power emergency personnel in making decisions and expected to be used in various practical scenarios.

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

Citation Download Citation

Zeyu Liu, Yitong Liu, Zehao Zhang, Lei Di, Feng Wei, and Yin Wang "Method for extracting power emergency plan information based on LLM prompt learning", Proc. SPIE 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024), 130800G (21 February 2024); https://doi.org/10.1117/12.3025216

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