LLM4Log: A Systematic Review of Large Language Model-based Log Analysis

Software systems generate massive, evolving, semi-structured logs that are central to reliability engineering and AIOps, yet difficult to analyze at scale under drift and limited labels. Recent advances in pretrained Transformer models and instructiontuned large language models (LLMs) have reshaped log analysis by enabling semantic generalization and cross-source evidence integration, but also introducing deployment risks such as context limits, latency and cost, privacy constraints, and hallucinations. This paper presents LLM4Log , a systematic review of LLM-based log analysis across the end-to-end pipeline, from upstream logging-statement generation and maintenance to log parsing/structuring and downstream tasks including anomaly detection, failure prediction, root cause analysis, and log summarization. Following a structured search and manual screening protocol, we completed literature collection in November 2025 and identified 145 unique papers across seven logging tasks. We organize the research area through a unified, task-driven taxonomy, summarize common design patterns (prompting/ICL, retrieval grounding, fine-tuning, tool/agent augmentation, and verification), and analyze evaluation practices, datasets, metrics, and reproducibility. Based on these cross-paper analyses, we summarize key lessons and open challenges for reliable real-world adoption. We emphasize robustness under drift and long-tail events, grounding and faithfulness for operator-facing outputs, and deployment-oriented designs with verifiable behavior.