A rule-aware modular prompt framework enables LLMs to perform structured numeric reasoning on power grid data by separating rules from normalized deviations, improving anomaly detection consistency and reducing token use in IEEE 118-bus tests.
arXiv preprint arXiv:2203.04291 (2022)
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ABHFA-Net is a novel few-shot classification framework that models prototypes as distributions, applies spatial-channel attention, and uses Bhattacharyya-based contrastive loss, achieving state-of-the-art accuracies on benchmark and disaster datasets.
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A Rule-Aware Prompt Framework for Structured Numeric Reasoning in Cyber-Physical Systems
A rule-aware modular prompt framework enables LLMs to perform structured numeric reasoning on power grid data by separating rules from normalized deviations, improving anomaly detection consistency and reducing token use in IEEE 118-bus tests.
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Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net
ABHFA-Net is a novel few-shot classification framework that models prototypes as distributions, applies spatial-channel attention, and uses Bhattacharyya-based contrastive loss, achieving state-of-the-art accuracies on benchmark and disaster datasets.