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arXiv Machine Learning By Bin Li, Dongdong Wang, Siyang Lu

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

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The paper investigates how different adaptation strategies, model architectures, parameter scales, and quantization settings influence the performance, efficiency, and robustness of large language models (LLMs) for log anomaly detection. Across three public log datasets, the study finds that adaptation strategies lead to significant performance variations, model scaling offers dataset‑dependent gains, and models with similar accuracy can differ markedly in computational cost. Low‑bit quantization largely preserves detection performance, and the authors also assess robustness to structural, semantic, and label noise at varying perturbation levels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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