  Early detection of power outages is crucial for maintaining a reliable power
distribution system. This research investigates the use of transfer learning
and language models in detecting outages with limited labeled data. By
leveraging pretraining and transfer learning, models can generalize to unseen
classes.
  Using a curated balanced dataset of social media tweets related to power
outages, we conducted experiments using zero-shot and few-shot learning. Our
hypothesis is that Language Models pretrained with limited data could achieve
high performance in outage detection tasks over baseline models. Results show
that while classical models outperform zero-shot Language Models, few-shot
fine-tuning significantly improves their performance. For example, with 10%
fine-tuning, BERT achieves 81.3% accuracy (+15.3%), and GPT achieves 74.5%
accuracy (+8.5%). This has practical implications for analyzing and localizing
outages in scenarios with limited data availability.
  Our evaluation provides insights into the potential of few-shot fine-tuning
with Language Models for power outage detection, highlighting their strengths
and limitations. This research contributes to the knowledge base of leveraging
advanced natural language processing techniques for managing critical
infrastructure.

Computation and Language;Applications