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Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
Author1, Author2, Author3, Author4, Author5
zero-shotrisk predictionmachine learninggeographic scalability
2609.04272
Builder Relevance
7h ago70%
Abstract
This study evaluates the effectiveness of large language models in predicting weather-related forced outages in the distribution grid without labeled training data.
Reality Card
Core Claim
Supervised models outperform large language models on macro-F1 and precision, but LLMs provide complementary strengths in actionable reasoning and geographic scalability.
Method / Result
Supervised models achieved higher macro-F1 and precision scores compared to LLMs.
Limitations
The study relies on a zero-shot framework, which may limit the generalizability of results across different contexts.
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