Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification
The evolution of communication technology has driven the demand for intelligent power
grids and data analysis in power systems. However, obtaining and annotating electrical data
from intelligent terminals is time-consuming and challenging. We propose Meta In-Context
Learning (M-ICL), a new approach that harnesses large language models to classify time
series electrical data, which largely alleviates the need for annotated data when adapting to
new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In …
grids and data analysis in power systems. However, obtaining and annotating electrical data
from intelligent terminals is time-consuming and challenging. We propose Meta In-Context
Learning (M-ICL), a new approach that harnesses large language models to classify time
series electrical data, which largely alleviates the need for annotated data when adapting to
new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In …