# LILAC: Log Parsing using LLMs with Adaptive Parsing Cache

arXiv: https://arxiv.org/abs/2310.01796 · HTML: https://mlsanity.com/paper/2310.01796/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2023.

## Built on by (most cited first, 30 of 32)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading](https://mlsanity.com/paper/2502.05370/) (2025)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://mlsanity.com/paper/2410.09352/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge](https://mlsanity.com/paper/2412.01377/) (2024)
- [On the Effectiveness of Large Language Models in Writing Alloy Formulas](https://mlsanity.com/paper/2502.15441/) (2025)
- [Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis](https://mlsanity.com/paper/2409.13561/) (2024)
- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [A Comparative Study on Large Language Models for Log Parsing](https://mlsanity.com/paper/2409.02474/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Models](https://mlsanity.com/paper/2408.01585/) (2024)
- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)
- [LLMLogAnalyzer: A Clustering-Based Log Analysis Chatbot using Large Language Models](https://mlsanity.com/paper/2510.24031/) (2025)
- [HELP: Hierarchical Embeddings-based Log Parsing](https://mlsanity.com/paper/2408.08300/) (2024)
- [SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs](https://mlsanity.com/paper/2505.14976/) (2025)
- [OBsmith: LLM-Powered JavaScript Obfuscator Testing](https://mlsanity.com/paper/2510.10066/) (2025)
- [Towards Secure Logging: Characterizing and Benchmarking Logging Code Security Issues with LLMs](https://mlsanity.com/paper/2604.20211/) (2026)
- [LanG -- A Governance-Aware Agentic AI Platform for Unified Security Operations](https://mlsanity.com/paper/2604.05440/) (2026)
- [TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps](https://mlsanity.com/paper/2609.14762/) (2026)
- [Fine Grained Insider Risk Detection](https://mlsanity.com/paper/2411.02645/) (2024)
- [A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition](https://mlsanity.com/paper/2506.01147/) (2025)
- [LLMBisect: Breaking Barriers in Bug Bisection with A Comparative Analysis Pipeline](https://mlsanity.com/paper/2510.26086/) (2025)
- [REFLEX: Reference-Free Evaluation of Log Summarization via Large Language Model Judgment](https://mlsanity.com/paper/2511.07458/) (2025)
- [Scalable and Efficient Large-Scale Log Analysis with LLMs: An IT Software Support Case Study](https://mlsanity.com/paper/2511.14803/) (2025)
- [MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning](https://mlsanity.com/paper/2601.07005/) (2026)
- [SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaboration](https://mlsanity.com/paper/2603.27247/) (2026)
- [TriFleetRCA: On-Premise LLM Root Cause Analysis for Kubernetes](https://mlsanity.com/paper/2609.23766/) (2026)

## Abstract (arXiv)

Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their accuracy in parsing. Moreover, the inherent inconsistent answers, as well as the substantial overhead, prevent the practical adoption of LLM-based log parsing. To address these challenges, we propose LILAC, the first practical log parsing framework using LLMs with adaptive parsing cache. To facilitate accurate and robust log parsing, LILAC leverages the in-context learning (ICL) capability of the LLM by performing a hierarchical candidate sampling algorithm and selecting high-quality demonstrations. Furthermore, LILAC incorporates a novel component, an adaptive parsing cache, to store and refine the templates generated by the LLM. It helps mitigate LLM's inefficiency issue by enabling rapid retrieval of previously processed log templates. In this process, LILAC adaptively updates the templates within the parsing cache to ensure the consistency of parsed results. The extensive evaluation on public large-scale datasets shows that LILAC outperforms state-of-the-art methods by 69.5% in terms of the average F1 score of template accuracy. In addition, LILAC reduces the query times to LLMs by several orders of magnitude, achieving a comparable efficiency to the fastest baseline.