Subir Majumder , Lin Dong , Fatemeh Doudi , Yuting Cai , Chao Tian , Dileep Kalathil , Kevin Ding , Anupam A. Thatte , Na Li , Le Xie
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引用次数: 0
Abstract
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.
期刊介绍:
Joule is a sister journal to Cell that focuses on research, analysis, and ideas related to sustainable energy. It aims to address the global challenge of the need for more sustainable energy solutions. Joule is a forward-looking journal that bridges disciplines and scales of energy research. It connects researchers and analysts working on scientific, technical, economic, policy, and social challenges related to sustainable energy. The journal covers a wide range of energy research, from fundamental laboratory studies on energy conversion and storage to global-level analysis. Joule aims to highlight and amplify the implications, challenges, and opportunities of novel energy research for different groups in the field.