LLMs show promise in predicting power outages, rivaling traditional ML

A new study published on arXiv evaluates the effectiveness of large language models (LLMs) in predicting weather-related power outages. The research formulated the problem as a binary severity classification task, comparing zero-shot LLMs against traditional supervised machine learning models. While supervised models generally outperformed LLMs in precision and macro-F1 scores, newer LLM generations showed competitive performance and offered additional benefits in actionable reasoning and geographic scalability. AI

IMPACT LLMs may offer new capabilities for critical infrastructure risk assessment and management.

RANK_REASON The cluster contains a research paper published on arXiv evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Hugging Face
• large-language models
• machine learning
• Texas

• paper
• model release
• infra

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