Log Parsing with Prompt-based Few-shot Learning
Open accessAbstract
Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.
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LogPPT reconstructs log parsing into a prompt-based few-shot learning task, accurately extracting templates and parameters with minimal annotations.
“LogPPT[37] reconstructs log parsing into a promptbased few-shot learning task, accurately extracting templates and parameters with minimal annotations.”
From From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps (2026) · cites this paper as [37]
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Construction of Software Supply Chain Threat Portrait Based on Chain Perspective
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CrashEventLLM: Predicting System Crashes with Large Language Models
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Dual Prompt-Based Few-Shot Learning for Automated Vulnerability Patch Localization
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Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era
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RetypeR: Integrated Retrieval-based Automatic Program Repair for Python Type Errors
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CodeDoctor: multi-category code review comment generation
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LLM-powered Zero-shot Online Log Parsing
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LogGenius: An Unsupervised Log Parsing Framework with Zero-shot Prompt Engineering
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Asking Questions about Scientific Articles—Identifying Large N Studies with LLMs
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LogTransformer: Transforming IT System Logs Into Events Using Tree-Based Approach
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Adaptive and Efficient Log Parsing as a Cloud Service
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FastRAG: Retrieval Augmented Generation for Semi-structured Data
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The Effectiveness of Compact Fine-Tuned LLMs in Log Parsing
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AI-Driven Log Analysis: Advances and Challenges
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EPAS: Efficient Online Log Parsing via Asynchronous Scheduling of LLM Queries
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A Novel Framework for Real-Time IP Reputation Validation Using Artificial Intelligence
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SCC-GPT: Source Code Classification Based on Generative Pre-Trained Transformers
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EvoParser: An Evolutionary Approach to Log Parsing
Setinský, J., & Žádník, M. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073703 · Cited by 2 · see citing works
SemanticLog: Towards Effective and Efficient Large-Scale Semantic Log Parsing
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Improving LLM-based Log Parsing by Learning from Errors in Reasoning Traces
Wang, J., Lu, J., Yang, J., Wang, J., Gao, Z., Zhang, J., Liang, Z., & Chang, E.-C. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00066 · Cited by 2 · see citing works
Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods
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Gloss: Guiding Large Language Models to Answer Questions from System Logs
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LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping
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Exploring Variable Potential for LLM-based Log Parsing Efficiency and Reduced Costs
Sun, J., Jia, T., He, M., Wu, Y., Li, Y., & Huang, G. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728506 · Cited by 3 · see citing works
LogSI: A Benchmark for System-Incremental Log Analysis
Zhou, M., Yang, W., Ma, L., Jiang, S., Xu, B., & Xiao, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890641 · Cited by 1 · see citing works
LogProb: Online Parsing Evolving Logs With Complex Parameters
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Next Steps in Cyber Blue Team Automation—Leveraging the Power of LLMs
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LLMLog: Advanced Log Template Generation via LLM-Driven Multi-Round Annotation
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Empirical Analysis of LLMDPP: Advancing Log Parsing in the LLM Era
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CSLParser: A Collaborative Framework Using Small and Large Language Models for Log Parsing
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Automated Logging With Large Language Models: Eliminating Task-Specific Pretraining
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GLU-PM: A Pretrained Model Toward Generalized Log Understanding
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LogExpertSolver: A Multi-Agent Framework for Domain-Specialized Log Parsing
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SCULP: An unsupervised LLM-based log parser with self-correcting capabilities
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Console Log Explainer: A Framework for Generating Automated Explanations using LLM
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From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps
Zhu, Y., Li, X., Suo, J., Yin, Y., & Huang, C. (2026) · 2026 International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508946
IPLog: An Efficient Log Parsing Method Based on Few-Shot Learning
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VOCE: A Virtual On-Call Engineer for Automated Alert Incident Analysis Using a Large Language Model
Chen, J., Chen, X., Shi, J., Wang, P., & Wang, W. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90900-9_4
Unsupervised Log Parsing Based on Large Language Models and Entropy
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Logcode: Source Code-Driven Log Parsing for Rare Patterns
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A Word is Worth 4-Bit: Efficient Log Parsing with Binary Coded Decimal Recognition
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BGformer: An Abnormal Event Detection Model For Distributed Software Operation And Maintenance Logs
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LogMerge: improved log parsing based on two-step clustering combined with low-level token processing
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MOLE: Provenance Graph Generation Framework Based on LLM Prompting
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Log Categorization Strategies for Scarce Data Scenarios
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Log Parsing with LLMs Featuring Self-reflection and Continuous Refining
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DLogParser: An Efficient Dynamic Log Parser with Multiple Grouping Criteria
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LogInstruct: Knowledge-Driven Instruction Synthesis for Enhancing LLM-Based Log Analysis
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Unsupervised, Accurate, and Efficient Log Parsing Using Smaller Open Source Large Language Models
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Can Large Language Models Improve SE Active Learning via Warm Starts?
Senthilkumar, L., & Menzies, T. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3796511
LogBoost: Boost Log Anomaly Detection by Cherry-Picking Log Sequences
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A Task-Specific Fine-Tuning Strategy and Evaluation for LLM-based Log Analysis
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Towards Automation in Log Parsing: Auto-Prompt Optimization with Natural Language Gradients
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Understanding Industrial Log Analysis: A Multi-Dataset Evaluation of Parsing and Anomaly Detection
Sun, Y., Keung, J., Liao, Y., & Yu, H. K. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00054
GO-MAF: A Human-in-the-Loop Multi-Agent Framework for Self-Healing 5/6G Network Operations
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Invited: Polymath: Self-Improving Hierarchical Workflow for Multi-Domain Problem Solving
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- Authors
- Le, V.-H., & Zhang, H.
- Affiliations
- University of Newcastle Australia (AU)
- Chongqing University (CN)
- Venue
- 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
- Publisher
- IEEE
- Published
- 2023-05
- Pages
- 2438-2449
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4384345672
- Cited by
- 129
- References
- 56
- Citations by year
- 2026: 40 · 2025: 51 · 2024: 30 · 2023: 8
- FWCI
- 38.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Australian Research Council (DP200102940,DP220103044)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Keywords
- log parsing, log templates, prompt tuning, log analysis, automated log analysis, training data selection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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