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Log Parsing with Prompt-based Few-shot Learning
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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[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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Zhang, X., & Wang, Y. (2025) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd67701.2025.11197991
Zhang, W., Cheng, X., Li, X., Yang, J., Zhang, L., Guan, X., & Li, Z. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761231
LogMerge: improved log parsing based on two-step clustering combined with low-level token processing
Le, H.-V., Nguyen, H., Huynh, N. K., & Phan, H. D. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02911-4
MOLE: Provenance Graph Generation Framework Based on LLM Prompting
Ren, Y., Wang, H., Chen, Y., Zhou, Z., Si, C., & Liu, Q. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229381
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Lipták, D., Felde, I., & Szénási, S. (2025) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti67731.2025.11311741
Log Parsing with LLMs Featuring Self-reflection and Continuous Refining
Chen, X., Chen, J., Shi, J., Wang, P., & Wang, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_18
DLogParser: An Efficient Dynamic Log Parser with Multiple Grouping Criteria
Yuan, J., Wang, C., Zhou, H., Zhang, Y., & Wang, Y. (2026) · Applied Sciences · doi:10.3390/app16020811
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Ma, L., Li, Y., Yang, W., Zhou, M., Zhao, M., Fei, B., Li, S., Jiang, S., & Xiao, Y. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3656204
Unsupervised, Accurate, and Efficient Log Parsing Using Smaller Open Source Large Language Models
Ma, Z., Kim, D. J., & Chen, T.-H. P. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3796239
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 · Cited by 10
LogBoost: Boost Log Anomaly Detection by Cherry-Picking Log Sequences
Li, M., Li, X., Chen, P., Lai, Y., & Zheng, Z. (2026) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2026.3664358
A Task-Specific Fine-Tuning Strategy and Evaluation for LLM-based Log Analysis
Li, B., Lu, S., Han, N., Wei, X., & Lu, W. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00172
Towards Automation in Log Parsing: Auto-Prompt Optimization with Natural Language Gradients
Chen, J., Hong, Y., Liu, J., & Bu, C. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7081-2_25
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
An, R., Jia, Z., Li, J., Dong, X. B., Zeng, Y., He, J., Wang, X., Bai, Z., Du, Y., & Wang, C. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11438074
Invited: Polymath: Self-Improving Hierarchical Workflow for Multi-Domain Problem Solving
Ho, C.-T., Gong, J., Yang, H., Akkur, A. B., & Ren, H. M. (2026) · International Symposium on Physical Design · doi:10.1145/3764386.3779639
Metadata
- 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 › Information Systems › 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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