能源行业
能量(信号处理)
工程类
计算机科学
环境经济学
经济
物理
量子力学
作者
Subir Majumder,Lin Dong,Fatemeh Doudi,Yuting Cai,Chao Tian,Dileep Kalathil,Ke Ding,Anupam A. Thatte,Na Li,Le Xie
出处
期刊:Joule
[Elsevier]
日期:2024-06-01
卷期号:8 (6): 1544-1549
被引量:3
标识
DOI:10.1016/j.joule.2024.05.009
摘要
Large language models (LLMs) as ChatBots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm toward adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this commentary identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases. Large language models (LLMs) as ChatBots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks. While there has been great enthusiasm toward adopting such foundational model-based artificial intelligence tools in all sectors possible, the capabilities and limitations of such LLMs in improving the operation of the electric energy sector need to be explored, and this commentary identifies fruitful directions in this regard. Key future research directions include data collection systems for fine-tuning LLMs, embedding power system-specific tools in the LLMs, and retrieval augmented generation (RAG)-based knowledge pool to improve the quality of LLM responses and LLMs in safety-critical use cases.
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