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

arXiv:2609.31371 · cs.LG, cs.CR · Submitted 2026-09-25 · Read on arXiv

cs.LG, cs.CR

Submitted: 2026-09-25

Updated: 2026-09-25

Comments: 6 pages, 2 figures, 3 tables. Submitted to IEEE ICASSP 2027

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 29× in this paper · explained in 1 episode
• Quantization — 22× in this paper · explained in 12 episodes
• Robustness — 20× in this paper · explained in 5 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Parameter-Efficient Fine-Tuning for Foundation Models
• LogLLM: Log-based Anomaly Detection Using Large Language Models
• RoBERTa: A Robustly Optimized BERT Pretraining Approach

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