The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 1×
“Hard prompt templates usually correspond to natural language phrases [41], in which each token in prompt templates is meaningful and understandable.”
BackgroundPrefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
Du, M., Li, F., Zheng, G., & Srikumar, V. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134015 · Cited by 1,835
Research works printed but not linked: 8
- 2022, Artifact for “guidelines for assessing the accuracy of log message template identification techniques”
- 10.18653/v1/P16-1162 · DOI · Paper page
- radford, 2019, Language models are unsupervised multitask learners, OpenAIRE blog, 1, 9
- 2021, A large collection of system log datasets for ai-powered log analytics
- 2022, A toolkit for automated log parsing
- radford, 2018, Improving language understanding by generative pre-training
- 2022, BGL Dataset
- 2022, HDFS dataset
Cited by
Sorted by citations
LILAC: Log Parsing using LLMs with Adaptive Parsing Cache
Jiang, Z., Liu, J., Chen, Z., Li, Y., Huang, J., Huo, Y., He, P., Gu, J., & Lyu, M. R.-T. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3643733 · Cited by 148
Cybersecurity Revolution via Large Language Models and Explainable AI
Ghazal, T. M., Janjua, J. I., Abushiba, W., Ahmad, M., Ihsan, A., & Al-Dmour, N. A. (2024) · International Conference on Security of Information and Networks (SIN) · doi:10.1109/sin63213.2024.10871324 · Cited by 97
Generative AI in mobile networks: a survey
Karapantelakis, A., Alizadeh, P., Alabbasi, A., Dey, K., & Nikou, A. (2023) · Annals of Telecommunications · doi:10.1007/s12243-023-00980-9 · Cited by 91
The Current Challenges of Software Engineering in the Era of Large Language Models
Gao, C., Hu, X., Gao, S., Xia, X., & Jin, Z. G. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712005 · Cited by 91
Log Parsing: How Far Can ChatGPT Go?
Le, V.-H., & Zhang, H. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00206 · Cited by 68
Self-citation
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
