How does Llama3's zero-shot performance in energy market anomaly detection compare to fine-tuned smaller

Abstract

Abstract: Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficiency Ratio (PER), a novel metric integrating accuracy, throughput, memory, and latency through geometric mean normalization. Our systematic evaluation reveals that small models (0.5–3B parameters) achieve superior PER scores across all given tasks. These findings establish quantita

Research Question

How does Llama3's zero-shot performance in energy market anomaly detection compare to fine-tuned smaller models in terms of F1 score and latency?

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Correction Record

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Provenance