Pith. sign in

REVIEW 1 major objections 2 minor 1 cited by

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

T0 review · 1 major / 2 minor · reviewed 2026-05-21 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read LLM techniques now cover the full log analysis pipeline from statement generation to anomaly detection and root cause analysis, per a review of 145 papers.

desk verdict This is a competent systematic review that organizes LLM log analysis into a task-driven taxonomy and flags practical challenges, but the search protocol lacks enough detail to confirm full coverage. read the letter →

arxiv 2604.16359 v2 pith:DCYCOXIS submitted 2026-03-18 cs.SE

Zeyang Ma , Jinqiu Yang , Tse-Hsun Chen This is my paper · ORCID
classification cs.SE
keywords largelanguagemodelsloganalysissystematicreviewanomalydetectionparsingrootcausesoftwarereliabilityAIOps
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper performs a systematic review to map the use of large language models across software log analysis. It examines the complete pipeline starting with logging statement generation and maintenance, moving through log parsing and structuring, and ending with downstream tasks such as anomaly detection, failure prediction, root cause analysis, and log summarization. The review extracts common design patterns including prompting, retrieval grounding, fine-tuning, tool augmentation, and verification, while also assessing evaluation methods, datasets, and reproducibility. A reader would care because logs drive reliability in large systems yet remain hard to analyze at scale, and LLMs introduce both new capabilities and risks such as hallucinations or high costs.

What carries the argument

The end-to-end pipeline from upstream logging-statement generation and maintenance to log parsing/structuring and downstream tasks, organized by a unified task-driven taxonomy across seven logging tasks.

What would settle it

An independent replication of the search protocol that yields substantially more or fewer papers or reveals major relevant works omitted from the collection would show the review does not represent the full literature.

Watch

Extended reading notes

Core claim

By following a structured search and screening protocol completed in November 2025, the authors identified 145 unique papers and organized them through a unified task-driven taxonomy. They summarize recurring design patterns such as prompting with in-context learning, retrieval grounding, fine-tuning, tool and agent augmentation, and output verification. The review further analyzes evaluation practices, datasets, metrics, and reproducibility issues, then derives key lessons and open challenges centered on robustness under drift and long-tail events, grounding and faithfulness for operator-facing outputs, and deployment-oriented designs with verifiable behavior.

Load-bearing premise

The structured search and manual screening protocol completed in November 2025 captured a representative and unbiased set of 145 papers without significant omissions.

Editorial extensions

If this is right

  • LLMs enable semantic generalization across evolving and semi-structured logs where traditional methods struggle.
  • Real-world deployment must address context limits, latency, cost, privacy constraints, and hallucinations.
  • Verification and grounding mechanisms are required to produce faithful outputs suitable for operators.
  • Evaluation practices need greater focus on long-tail events and robustness under data drift.
  • Standardization of datasets and metrics would improve reproducibility across studies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Hybrid approaches that pair LLMs with conventional structured parsers could reduce hallucinations while retaining semantic strengths.
  • Industry teams may need explicit decision criteria for choosing LLM-based log tools over established statistical methods in production.
  • Targeted experiments could measure how specific verification techniques affect faithfulness on public log datasets under controlled drift.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 2 minor

Summary. This paper presents LLM4Log, a systematic review of LLM-based log analysis covering the full pipeline from upstream logging-statement generation and maintenance through log parsing/structuring to downstream tasks such as anomaly detection, failure prediction, root cause analysis, and log summarization. Following a structured search and manual screening protocol completed in November 2025, the authors identify 145 unique papers, organize them via a task-driven taxonomy, summarize design patterns (prompting/ICL, retrieval grounding, fine-tuning, tool/agent augmentation, verification), analyze evaluation practices/datasets/metrics/reproducibility, and distill key lessons plus open challenges for reliable adoption, with emphasis on robustness under drift, grounding/faithfulness, and verifiable deployment.

Significance. If the 145-paper corpus is representative, the review would be a useful synthesis of an emerging area at the intersection of LLMs and AIOps/reliability engineering. It could help researchers and practitioners by unifying disparate tasks under one taxonomy, cataloging reusable design patterns, and surfacing cross-cutting issues such as context limits, hallucinations, and long-tail robustness. The focus on reproducibility, datasets, and deployment-oriented challenges adds practical value beyond a simple enumeration of papers.

major comments (1)
  1. [§3] §3 (Literature Search and Screening): The central claim that the structured search and manual screening protocol yielded a representative set of 145 unique papers is load-bearing for the taxonomy, lessons, and open challenges. However, the manuscript does not provide the exact search strings, queried databases, time bounds, full inclusion/exclusion criteria, or inter-rater reliability statistics. Without these details it is impossible to assess selection bias or omissions (e.g., recent arXiv-only or non-English work), directly undermining confidence in the completeness of the synthesis.
minor comments (2)
  1. [Abstract, §1] Abstract and §1: The literature collection date is given as November 2025. Clarify whether this is the actual completion date or a projected one, and ensure consistency with the submission timeline.
  2. [Throughout] Throughout: Some citations to the 145 papers appear only in tables or supplementary material; ensure every referenced work is explicitly cited in the main text on first mention for traceability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the potential utility of LLM4Log as a synthesis of an emerging area. We agree that methodological transparency is essential for a systematic review and will revise the manuscript accordingly to address the concern raised.

read point-by-point responses

  1. Referee: [§3] §3 (Literature Search and Screening): The central claim that the structured search and manual screening protocol yielded a representative set of 145 unique papers is load-bearing for the taxonomy, lessons, and open challenges. However, the manuscript does not provide the exact search strings, queried databases, time bounds, full inclusion/exclusion criteria, or inter-rater reliability statistics. Without these details it is impossible to assess selection bias or omissions (e.g., recent arXiv-only or non-English work), directly undermining confidence in the completeness of the synthesis.

    Authors: We acknowledge that the current version of the manuscript does not include the full details of the search protocol, which limits the ability to evaluate potential selection biases or omissions. We agree this information is necessary to support the claim of a representative corpus. In the revised manuscript we will expand Section 3 (and add an appendix if space is constrained) to report: the precise search strings employed across each database, the complete list of queried sources (arXiv, Google Scholar, ACM Digital Library, IEEE Xplore, and any others), the time bounds (literature collected through November 2025), the full inclusion and exclusion criteria applied at each screening stage, and any inter-rater reliability statistics or justification for their absence. These additions will directly address concerns about representativeness, including coverage of recent arXiv-only or non-English work. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity in systematic literature review synthesis

full rationale

This paper is a systematic review that identifies and synthesizes 145 external papers on LLM-based log analysis via a described structured search and manual screening protocol completed in November 2025. No mathematical derivations, fitted parameters, predictions, or self-referential definitions appear in the provided text. The central claims report on external literature findings, design patterns, and challenges rather than reducing any result to the authors' own inputs or prior self-citations by construction. The search protocol is presented as an independent method for literature collection and does not exhibit self-definition or load-bearing reduction to unverified self-references.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The review rests on standard systematic-review methodology assumptions rather than new free parameters or invented entities.

assumptions (1)
  • domain assumption A structured search combined with manual screening can produce a representative sample of the LLM-for-log-analysis literature.
    Invoked when stating that 145 unique papers were identified after literature collection in November 2025.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LLM4Log: A Systematic Review of Large Language Model-based Log Analysis." pith.science (2026). https://pith.science/paper/DCYCOXIS

@misc{pith2026260416359,
  author       = {Pith},
  title        = {Pith review of: LLM4Log: A Systematic Review of Large Language Model-based Log Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DCYCOXIS}},
  note         = {Machine review of arXiv:2604.16359}
}
read the original abstract

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 instruction-tuned 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.

Figures

Figures reproduced from arXiv: 2604.16359 by the authors.

Figure 1
Figure 1. LLM-based Log Analysis Across the Pipeline: From Logging to Downstream Tasks [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Temporal and overall distribution of LLM4Log analysis papers across tasks. Note: Pie chart labels are shown as n (u), [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. An example log parsing result from Hadoop. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Common workflow for LLM-enabled downstream log analysis. [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]
Figure 5
Figure 5. Figure 5: An illustrative overview of log window representations: raw logs are parsed into templates and parameters, then [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LogSemFuse: Semantic Evidence Fusion for Explainable Log Anomaly Detection

    cs.SE 2026-07 conditional novelty 5.0 of 10

    A plug-in fuses backbone scores with n-gram patterns, LLM risk labels, and cluster-derived rules, recovering 98.8% of false negatives and preferred explanations on HDFS, BGL, and Liberty.

Reference graph

Works this paper leans on

224 extracted references · 224 canonical work pages · cited by 1 Pith paper

  1. [1]

    Llm-based event log analysis techniques: A survey.arXiv preprint arXiv:2502.00677, 2025

    Siraaj Akhtar, Saad Khan, and Simon Parkinson. Llm-based event log analysis techniques: A survey.arXiv preprint arXiv:2502.00677, 2025

    work page arXiv 2025

  2. [2]

    Logfit: Log anomaly detection using fine-tuned language models.IEEE Transactions on Network and Service Management, 21(2):1715–1723, 2024

    Crispin Almodovar, Fariza Sabrina, Sarvnaz Karimi, and Salahuddin Azad. Logfit: Log anomaly detection using fine-tuned language models.IEEE Transactions on Network and Service Management, 21(2):1715–1723, 2024

    work page 2024

  3. [3]

    Apache log4j 2

    Apache Software Foundation. Apache log4j 2. Online documentation, 2024. URL https://logging.apache.org/log4j/2.x/

    work page 2024

  4. [4]

    A comparative study on large language models for log parsing

    Merve Astekin, Max Hort, and Leon Moonen. A comparative study on large language models for log parsing. InProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2024), pages 36–47. ACM, 2024. doi: 10.1145/3674805.3686684. URL https://doi.org/10.1145/3674805.3686684

    work page doi:10.1145/3674805.3686684 2024

  5. [5]

    AnomalyExplainerBot: Explainable AI for LLM-based anomaly detection using BERTViz & Captum, 2025

    Prasasthy Balasubramanian, Dumindu Kankanamge, Ekaterina Gilman, and Mourad Oussalah. AnomalyExplainerBot: Explainable AI for LLM-based anomaly detection using BERTViz & Captum, 2025

    work page 2025

  6. [6]

    System log parsing with large language models: A review.arXiv preprint arXiv:2504.04877, 2025

    Viktor Beck, Max Landauer, Markus Wurzenberger, Florian Skopik, and Andreas Rauber. System log parsing with large language models: A review.arXiv preprint arXiv:2504.04877, 2025. 37

    work page arXiv 2025

  7. [7]

    In: 2025 13th ISDFS (2025)

    Sidahmed Benabderrahmane, Petko Valtchev, James Cheney, and Talal Rahwan. APT-LLM: Embedding-based anomaly detection of cyber advanced persistent threats using large language models. InProceedings of the 13th International Symposium on Digital Forensics and Security (ISDFS 2025), pages 1–6, 2025. doi: 10.1109/ISDFS65363.2025.11011912

    work page doi:10.1109/isdfs65363.2025.11011912 2025

  8. [8]

    Bengio, P

    Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166, 1994. doi: 10.1109/72.279181

    work page doi:10.1109/72.279181 1994

Show all 224 references
  1. [9]

    Auto-logging: Ai-centred logging instrumentation

    Jasmin Bogatinovski and Odej Kao. Auto-logging: Ai-centred logging instrumentation. In2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pages 95–100, 2023. doi: 10 .1109/ICSE-NIER58687.2023.00023. URL https://doi.o...

    2023 doi

  2. [10]

    Good enough to learn: LLM-based anomaly detection in ECU logs without reliable labels, 2025

    Bogdan Bogdan, Arina Cazacu, and Laura Vasilie. Good enough to learn: LLM-based anomaly detection in ECU logs without reliable labels, 2025. URL https://arxiv.org/abs/2507.01077. Accepted to IEEE Intelligent Vehicles Symposium (IV) 2025

    2025

  3. [11]

    Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S

    Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jare...

    2021 arXiv

  4. [12]

    Language models are few-shot learners.Advances in neural information processing systems, 33: 1877–1901, 2020

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advances in neural information processing systems, 33: 1877–1901, 2020

    1901

  5. [13]

    Extracting training data from large language models

    Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. Extracting training data from large language models. In30th USENIX Security Symposium (USE...

    2021

  6. [14]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott G...

    2021 arXiv

  7. [15]

    Bert-log: Anomaly detection for system logs based on pre-trained language model.Applied Artificial Intelligence, 36(1):2145642, 2022

    Song Chen and Hai Liao. Bert-log: Anomaly detection for system logs based on pre-trained language model.Applied Artificial Intelligence, 36(1):2145642, 2022. doi: 10 .1080/08839514.2022.2145642. URL https://www .tandfonline.com/doi/full/10.1080/08839514.2022.2145642

    2022 doi

  8. [16]

    Epas: Efficient online log parsing via asynchronous scheduling of llm queries

    Xiaolei Chen, Jie Shi, Jia Chen, Peng Wang, and Wei Wang. Epas: Efficient online log parsing via asynchronous scheduling of llm queries. InProceedings of the 41st IEEE International Conference on Data Engineering (ICDE 2025), pages 4025–4037. IEEE, 2025. doi: 10.1109/ICDE.2025...

    2025 doi

  9. [17]

    Automatic root cause analysis via large language models for cloud incidents

    Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang, Xin Gao, Liu Shi, Yunjie Cao, Xuedong Gao, Hao Fan, Ming Wen, et al. Automatic root cause analysis via large language models for cloud incidents. InProceedings of the Nineteenth European Conference on Computer Systems, pages 674...

    2024

  10. [18]

    Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei

    Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. InAdvances in Neural Information Processing Systems, volume 30, 2017. URL https://papers .neurips.cc/paper/7017-deep- reinforcement-learni...

    2017

  11. [19]

    Electra: Pre-training text encoders as discriminators rather than generators.arXiv preprint arXiv:2003.10555, 2020

    Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. Electra: Pre-training text encoders as discriminators rather than generators.arXiv preprint arXiv:2003.10555, 2020

    2003 arXiv

  12. [20]

    Semantic hierarchical classification applied to anomaly detection using system logs with a bert model.Applied Sciences, 14(13), 2024

    Clara Corbelle, Victor Carneiro, and Fidel Cacheda. Semantic hierarchical classification applied to anomaly detection using system logs with a bert model.Applied Sciences, 14(13), 2024. ISSN 2076-3417. doi: 10 .3390/app14135388. URL https://www .mdpi.com/2076- 3417/14/13/5388

    2024

  13. [21]

    Aetherlog: Log-based root cause analysis by integrating large language models with knowledge graphs

    Tianyu Cui, Ruowei Fu, Changchang Liu, Yuhe Ji, Wenwei Gu, Shenglin Zhang, Yongqian Sun, and Dan Pei. Aetherlog: Log-based root cause analysis by integrating large language models with knowledge graphs. In2025 IEEE 36th International Symposium on Software Reliability Engineeri...

    2025

  14. [22]

    Logeval: A comprehensive benchmark suite for llms in log analysis.Empirical Softw

    Tianyu Cui, Shiyu Ma, Ziang Chen, Tong Xiao, Chenyu Zhao, Shimin Tao, Yilun Liu, Shenglin Zhang, Duoming Lin, Changchang Liu, Yuzhe Cai, Weibin Meng, Yongqian Sun, and Dan Pei. Logeval: A comprehensive benchmark suite for llms in log analysis.Empirical Softw. Engg., 30(6), Oct...

    2025 doi

  15. [23]

    Logram: Efficient log parsing using𝑛 n-gram dictionaries

    Hetong Dai, Heng Li, Che-Shao Chen, Weiyi Shang, and Tse-Hsun Chen. Logram: Efficient log parsing using𝑛 n-gram dictionaries. IEEE Transactions on Software Engineering, 48(3):879–892, 2020

    2020

  16. [24]

    loguru: Python logging made simple

    Delgan and contributors. loguru: Python logging made simple. Online documentation, 2024. URL https://github .com/Delgan/loguru. 38

    2024

  17. [25]

    Qlora: Efficient finetuning of quantized llms, 2023

    Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. Qlora: Efficient finetuning of quantized llms, 2023. URL https://arxiv.org/abs/2305.14314

    2023 arXiv

  18. [26]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human langua...

    2019

  19. [27]

    Pdlogger: Automated logging framework for practical software development, 2025

    Shengcheng Duan, Yihua Xu, Sheng Zhang, Shen Wang, and Yue Duan. Pdlogger: Automated logging framework for practical software development, 2025. URL https://arxiv.org/abs/2507.19951

    2025

  20. [28]

    Unsupervised log parsing based on large language models and entropy

    Yiqi Duan, Jianliang Xu, Changyu Fan, and Zixin Liu. Unsupervised log parsing based on large language models and entropy. In2025 11th International Symposium on System Security, Safety, and Reliability (ISSSR), pages 1–10. IEEE, 2025

    2025

  21. [29]

    Early exploration of using ChatGPT for log-based anomaly detection on parallel file systems logs

    Chris Egersdoerfer, Di Zhang, and Dong Dai. Early exploration of using ChatGPT for log-based anomaly detection on parallel file systems logs. InProceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC ’23), pages 315–316. Ass...

    2023 doi

  22. [30]

    Insightai: Root cause analysis in large log files with private data using large language model

    Maryam Ekhlasi, Anurag Prakash, Maxime Lamothe, and Michel Dagenais. Insightai: Root cause analysis in large log files with private data using large language model. In2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN), pages 31–41...

    2025 doi

  23. [31]

    A novel framework for detecting anomalies in network security using LLM and deep learning.Journal of Electrical Systems, 21(1s):294–302, 2025

    Mahmoudreza Entezami, Shahabeddin Rahimi Harsini, David Houshangi, and Zahra Entezami. A novel framework for detecting anomalies in network security using LLM and deep learning.Journal of Electrical Systems, 21(1s):294–302, 2025. doi: 10 .52783/jes.8791

    2025

  24. [32]

    How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings

    Kawin Ethayarajh. How contextual are contextualized word representations? comparing the geometry of BERT, ELMo, and GPT-2 embeddings. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Empirica...

    2019 doi

  25. [33]

    Regulation (eu) 2016/679 of the european parliament and of the council (general data protection regulation)

    European Parliament and Council of the European Union. Regulation (eu) 2016/679 of the european parliament and of the council (general data protection regulation). Official Journal of the European Union, L119, 2016. URL https://eur-lex.europa.eu/eli/reg/2016/679/oj

    2016

  26. [34]

    Log anomaly detection by leveraging llm-based parsing and embedding with attention mechanism

    Asma Fariha, Vida Gharavian, Masoud Makrehchi, Shahryar Rahnamayan, Sanaa Alwidian, and Akramul Azim. Log anomaly detection by leveraging llm-based parsing and embedding with attention mechanism. In2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), p...

    2024

  27. [35]

    Codebert: A pre-trained model for programming and natural languages

    Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. Codebert: A pre-trained model for programming and natural languages. InFindings of the Association for Computational Linguistics: EMNLP 2020, p...

    2020

  28. [36]

    Security and privacy controls for information systems and organizations (nist special publication 800-53 revision 5),

    Joint Task Force. Security and privacy controls for information systems and organizations (nist special publication 800-53 revision 5),

  29. [37]

    URL https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final

  30. [38]

    Amy Foster and Selva Kumar.COMBINING LLMS AND SHELL LOGS TO PREDICT BACKUP FAILURES. 07 2025

    2025

  31. [39]

    Where do developers log? an empirical study on logging practices in industry

    Qiang Fu, Jieming Zhu, Wenlu Hu, Jian-Guang Lou, Rui Ding, Qingwei Lin, Dongmei Zhang, and Tao Xie. Where do developers log? an empirical study on logging practices in industry. InCompanion Proceedings of the 36th International Conference on Software Engineering, pages 24–33. ...

    2014 doi

  32. [40]

    End-to-end log statement generation at block-level.Journal of Systems and Software, 216:112146, 2024

    Ying Fu, Meng Yan, Pinjia He, Chao Liu, Xiaohong Zhang, and Dan Yang. End-to-end log statement generation at block-level.Journal of Systems and Software, 216:112146, 2024. doi: 10.1016/j.jss.2024.112146

    2024 doi

  33. [41]

    Not what you ´ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection, 2023

    Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christopher Endres, Thorsten Holz, and Mario Fritz. Not what you ´ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection, 2023. URL https://arxiv.org/abs/2302.12173

    2023 arXiv

  34. [42]

    LogLLM: Log-based anomaly detection using large language models.arXiv preprint,

    Wei Guan, Jian Cao, Shiyou Qian, and Jianqi Gao. LogLLM: Log-based anomaly detection using large language models.arXiv preprint,

  35. [43]

    URL https://arxiv.org/abs/2411.08561

  36. [44]

    H. Guo, S. Yuan, and X. Wu. Logbert: Log anomaly detection via bert. In2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2021

    2021

  37. [45]

    Logformer: A pre-train and tuning pipeline for log anomaly detection

    Hongcheng Guo, Jian Yang, Jiaheng Liu, Jiaqi Bai, Boyang Wang, Zhoujun Li, Tieqiao Zheng, Bo Zhang, Junran Peng, and Qi Tian. Logformer: A pre-train and tuning pipeline for log anomaly detection. InProceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 135...

    2024

  38. [46]

    Owl: A large language model for it operations

    Hongcheng Guo, Jian Yang, Jiaheng Liu, Liqun Yang, Linzheng Chai, Jiaqi Bai, Junran Peng, Xiaorong Hu, Chao Chen, Dongfeng Zhang, Xu Shi, Tieqiao Zheng, Liangfan Zheng, Bo Zhang, Ke Xu, and Zhoujun Li. Owl: A large language model for it operations. InProceedings of the Twelfth...

    2024

  39. [47]

    Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. Don’t stop pretraining: Adapt language models to domains and tasks. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages...

    2020

  40. [48]

    Llm meets ml: Data-efficient anomaly detection on unstable logs.ACM Transactions on Software Engineering and Methodology, 2025

    Fatemeh Hadadi, Qinghua Xu, Domenico Bianculli, and Lionel Briand. Llm meets ml: Data-efficient anomaly detection on unstable logs.ACM Transactions on Software Engineering and Methodology, 2025. doi: 10 .1145/3771283. URL https://doi .org/10.1145/3771283. arXiv:2406.07467

    2025 doi

  41. [49]

    Llmelog: An approach for anomaly detection based on llm-enriched log events

    Minghua He, Tong Jia, Chiming Duan, Huaqian Cai, Ying Li, and Gang Huang. Llmelog: An approach for anomaly detection based on llm-enriched log events. In2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), pages 132–143. IEEE, 2024

    2024

  42. [50]

    Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R. Lyu. Drain: An online log parsing approach with fixed depth tree. In2017 IEEE International Conference on Web Services (ICWS), pages 33–40, 2017. doi: 10.1109/ICWS.2017.13

    2017 doi

  43. [51]

    A survey on automated log analysis for reliability engineering.ACM computing surveys (CSUR), 54(6):1–37, 2021

    Shilin He, Pinjia He, Zhuangbin Chen, Tianyi Yang, Yuxin Su, and Michael R Lyu. A survey on automated log analysis for reliability engineering.ACM computing surveys (CSUR), 54(6):1–37, 2021

    2021

  44. [52]

    Parameter-efficient log anomaly detection based on pre-training model and lora

    Shiming He, Ying Lei, Ying Zhang, Kun Xie, and Pradip Kumar Sharma. Parameter-efficient log anomaly detection based on pre-training model and lora. In2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pages 207–217. IEEE, 2023

    2023

  45. [53]

    Benchmarking open-source large language models for log level suggestion

    Yi Wen Heng, Zeyang Ma, Zhenhao Li, Dong Jae Kim, and Tse-Hsun Chen. Benchmarking open-source large language models for log level suggestion. In2025 IEEE Conference on Software Testing, Verification and Validation (ICST), pages 314–325, 2025. doi: 10.1109/ICST62969.2025.10988921

    2025 doi

  46. [54]

    Diagnosing robotics systems issues with large language models–a case study

    Jordis Emilia Herrmann, Aswath Mandakath Gopinath, Mikael Norrlof, and Mark Niklas Mueller. Diagnosing robotics systems issues with large language models–a case study. InICLR 2025 Workshop on Foundation Models in the Wild

    2025

  47. [55]

    Long short-term memory.Neural Computation, 9(8):1735–1780, 1997

    Sepp Hochreiter and J"urgen Schmidhuber. Long short-term memory.Neural Computation, 9(8):1735–1780, 1997. doi: 10 .1162/ neco.1997.9.8.1735

    1997

  48. [56]

    Reguly, Kálmán Tornai, Tamás Zsedrovits, and Zoltán Máthé

    András Horváth, András Oláh, Attila Pintér, Bálint Siklósi, Gergely Lukács, István Z. Reguly, Kálmán Tornai, Tamás Zsedrovits, and Zoltán Máthé. Anomaly detection algorithms for real-time log data analysis at scale.IEEE Access, 13:136288–136311, 2025. doi: 10.1109/ACCESS.2025.3565575

    2025 doi

  49. [57]

    Research on log anomaly detection based on sentence-BERT.Electronics, 12(17):3580, 2023

    Changze Hu, Yu Fang, Jinhua Wu, Haoyang Li, and Geng Wang. Research on log anomaly detection based on sentence-BERT.Electronics, 12(17):3580, 2023. doi: 10.3390/electronics12173580. URL https://www.mdpi.com/2079-9292/12/17/3580

    2023 doi

  50. [58]

    Demystifying and extracting fault-indicating information from logs for failure diagnosis

    Junjie Huang, Zhihan Jiang, Jinyang Liu, Yintong Huo, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Hui Dong, Zengyin Yang, and Michael R Lyu. Demystifying and extracting fault-indicating information from logs for failure diagnosis. In2024 IEEE 35th International Symposium on Softwar...

    2024

  51. [59]

    Junjie Huang, Zhihan Jiang, Zhuangbin Chen, and Michael R. Lyu. No more labelled examples? an unsupervised log parser with llms. InProceedings of the ACM on Software Engineering (FSE 2025), number FSE. ACM, 2025. doi: 10 .1145/3729377. URL https: //dl.acm.org/doi/10.1145/3729377

    2025 doi

  52. [60]

    Fung, Rong He, Yining Zhao, Hailong Yang, and Zhongzhi Luan

    Shaohan Huang, Yi Liu, Carol J. Fung, Rong He, Yining Zhao, Hailong Yang, and Zhongzhi Luan. Hitanomaly: Hierarchical transformers for anomaly detection in system log.IEEE Transactions on Network and Service Management, 17(2):2064–2076, 2020. doi: 10 .1109/ TNSM.2020.3034647

  53. [61]

    Fung, He Wang, Hailong Yang, and Zhongzhi Luan

    Shaohan Huang, Yi Liu, Carol J. Fung, He Wang, Hailong Yang, and Zhongzhi Luan. Improving log-based anomaly detection by pre-training hierarchical transformers.IEEE Transactions on Computers, 72(9):2656–2667, 2023. doi: 10.1109/TC.2023.3257518

    2023 doi

  54. [62]

    LogRules: Enhancing log analysis capability of large language models through rules

    Xin Huang, Ting Zhang, and Wen Zhao. LogRules: Enhancing log analysis capability of large language models through rules. In Luis Chiruzzo, Alan Ritter, and Lu Wang, editors,Findings of the Association for Computational Linguistics: NAACL 2025, pages 452– 470, Albuquerque, New ...

    2025

  55. [63]

    Xuanbo Huang, Kaiping Xue, Lutong Chen, Jiangping Han, Jian Li, and David S. L. Wei. Forensix: Automated network forensics and diagnostics for beyond-5g and 6g networks using large language models.IEEE Network, 39(5):74–80, 2025. doi: 10 .1109/ MNET.2025.3579925

    2025

  56. [64]

    Yintong Huo, Cheryl Lee, Yuxin Su, Shiwen Shan, Jinyang Liu, and Michael R. Lyu. Evlog: Evolving log analyzer for anomalous logs identification. arXiv preprint arXiv:2306.01509, 2023. URL https://arxiv.org/abs/2306.01509

    2023

  57. [65]

    Fall: Prior failure detection in large scale system based on language model.IEEE Transactions on Dependable and Secure Computing, 22(1):279–291, 2025

    Jaeyoon Jeong, Insung Baek, Byungwoo Bang, Junyeon Lee, Uiseok Song, and Seoung Bum Kim. Fall: Prior failure detection in large scale system based on language model.IEEE Transactions on Dependable and Secure Computing, 22(1):279–291, 2025. doi: 10.1109/TDSC.2024.3396166

    2025 doi

  58. [66]

    Lemad: LLM-empowered multi-agent system for anomaly detection in power grid services.Electronics, 14(15):3008, 2025

    Xin Ji, Le Zhang, Wenya Zhang, Fang Peng, Yifan Mao, Xingchuang Liao, and Kui Zhang. Lemad: LLM-empowered multi-agent system for anomaly detection in power grid services.Electronics, 14(15):3008, 2025. doi: 10 .3390/electronics14153008. URL https: //doi.org/10.3390/electronics14153008

    2025 doi

  59. [67]

    Adapting large language models to log analysis with interpretable domain knowledge

    Yuhe Ji, Yilun Liu, Feiyu Yao, Minggui He, Shimin Tao, Xiaofeng Zhao, Chang Su, Xinhua Yang, Weibin Meng, Yuming Xie, Boxing Chen, Shenglin Zhang, and Yongqian Sun. Adapting large language models to log analysis with interpretable domain knowledge. InProceedings of the 34th AC...

    2025 doi

  60. [68]

    Survey of hallucination in natural language generation, 2023

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tianle Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation, 2023. URL https://arxiv.org/abs/2304.04710

    2023

  61. [69]

    Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, and Michael R. Lyu. Lilac: Log parsing using llms with adaptive parsing cache.Proceedings of the ACM on Software Engineering, 1(FSE), 2024. doi: 10 .1145/3643733. URL https:...

    2024 doi

  62. [70]

    Language models (mostly) know what they know, 2022

    Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Dawn Drain, Nelson Chen, Yuntao Bai, Jared Kaplan, Sam McCandlish, Dario Amodei, Ethan Chen, and Catherine Olsson. Language models (mostly) know what they know, 202...

    2022 arXiv

  63. [71]

    Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020. URL https://arxiv .org/abs/2001.08361

    2001 arXiv

  64. [72]

    Exploring semantic vs

    Crystal Karlsen, Denis Copstein, Yue Luo, Benjamin Schwartzentruber, Tim Niblett, and Olivier Rouyer. Exploring semantic vs. syntactic features for unsupervised learning on application log files. In2023 International Conference on Cyber Security and Networks (CSNet), 2023. doi...

    2023 doi

  65. [73]

    Benchmarking large language models for log analysis, security, and interpretation.Journal of Network and Systems Management, 32:59, 2024

    Egil Karlsen, Xiao Luo, Nur Zincir-Heywood, and Malcolm Heywood. Benchmarking large language models for log analysis, security, and interpretation.Journal of Network and Systems Management, 32:59, 2024. doi: 10.1007/s10922-024-09831-x

    2024 doi

  66. [74]

    Dense passage retrieval for open-domain question answering

    Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages...

    2020

  67. [75]

    Nist special publication 800-92: Guide to computer security log management, 2006

    Karen Kent and Murugiah Souppaya. Nist special publication 800-92: Guide to computer security log management, 2006. URL https://csrc.nist.gov/publications/detail/sp/800-92/final

    2006

  68. [76]

    Log-based anomaly detection without log parsing

    Van-Hoang Le and Hongyu Zhang. Log-based anomaly detection without log parsing. In2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE/ACM, 2021

    2021

  69. [77]

    Log parsing with prompt-based few-shot learning

    Van-Hoang Le and Hongyu Zhang. Log parsing with prompt-based few-shot learning. InProceedings of the 45th International Conference on Software Engineering (ICSE 2023), pages 2438–2449. IEEE, 2023. doi: 10 .1109/ICSE48619.2023.00204. URL https://conf .researchr.org/ details/ics...

    2023

  70. [78]

    Log parsing: How far can chatgpt go? InProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, ASE ’23, page 1699–1704

    Van-Hoang Le and Hongyu Zhang. Log parsing: How far can chatgpt go? InProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, ASE ’23, page 1699–1704. IEEE Press, 2024. ISBN 9798350329964. doi: 10.1109/ASE56229.2023.00206. URL https://doi.o...

    2024 doi

  71. [79]

    Prelog: A pre-trained model for log analytics.Proceedings of the ACM on Management of Data, 2(3): 1–28, 2024

    Van-Hoang Le and Hongyu Zhang. Prelog: A pre-trained model for log analytics.Proceedings of the ACM on Management of Data, 2(3): 1–28, 2024. doi: 10.1145/3654966. URL https://dl.acm.org/doi/10.1145/3654966. Presented at SIGMOD 2024

    2024 doi

  72. [80]

    Unleashing the true potential of semantic-based log parsing with pre-trained language models

    Van-Hoang Le, Yi Xiao, and Hongyu Zhang. Unleashing the true potential of semantic-based log parsing with pre-trained language models. InProceedings of the 47th International Conference on Software Engineering (ICSE 2025). IEEE/ACM, 2025. URL https://conf .researchr.org/detail...

    2025

  73. [81]

    Logresp-agent: A recursive ai framework for context-aware log anomaly detection and ttp analysis.Applied Sciences, 15(13), 2025

    Juyoung Lee, Yeonsu Jeong, Taehyun Han, and Taejin Lee. Logresp-agent: A recursive ai framework for context-aware log anomaly detection and ttp analysis.Applied Sciences, 15(13), 2025. ISSN 2076-3417. doi: 10 .3390/app15137237. URL https://www .mdpi.com/2076- 3417/15/13/7237

    2025

  74. [82]

    Lanobert: System log anomaly detection based on bert masked language model, 2021

    Yukyung Lee, Jina Kim, and Pilsung Kang. Lanobert: System log anomaly detection based on bert masked language model, 2021. URL https://arxiv.org/abs/2111.09564

    2021

  75. [83]

    Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th annu...

    2020

  76. [84]

    uttler, Mike Lewis, Wen-tau Yih, Tim Rockt

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K"uttler, Mike Lewis, Wen-tau Yih, Tim Rockt"aschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks, 2020. URL https://a...

    2020 arXiv

  77. [85]

    Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults

    Xiaoyun Li, Pengfei Chen, Linxiao Jing, Zilong He, and Guangba Yu. Swisslog: Robust and unified deep learning based log anomaly detection for diverse faults. In2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), pages 92–103. IEEE, 2020

    2020

  78. [86]

    AnomalyGen: An automated semantic log sequence generation framework with LLM for anomaly detection, 2025

    Xinyu Li, Yingtong Huo, Chenxi Mao, Shiwen Shan, Yuxin Su, Dan Li, and Zibin Zheng. AnomalyGen: An automated semantic log sequence generation framework with LLM for anomaly detection, 2025. URL https://arxiv.org/abs/2504.12250

    2025

  79. [87]

    Exploring the effectiveness of llms in automated logging statement generation: An empirical study.IEEE Transactions on Software Engineering, 2024

    Yichen Li, Yintong Huo, Zhihan Jiang, Renyi Zhong, Pinjia He, Yuxin Su, Lionel C Briand, and Michael R Lyu. Exploring the effectiveness of llms in automated logging statement generation: An empirical study.IEEE Transactions on Software Engineering, 2024

    2024

  80. [88]

    Yichen Li, Yintong Huo, Renyi Zhong, Zhihan Jiang, Jinyang Liu, Junjie Huang, Jiazhen Gu, Pinjia He, and Michael R. Lyu. Go static: Contextualized logging statement generation.Proceedings of the ACM on Software Engineering, 1(FSE):609–630, 2024. doi: 41 10.1145/3643754. URL ht...

    2024 doi

  81. [89]

    Automated proactive logging quality improvement for large-scale codebases

    Yichen Li, Jinyang Liu, Junsong Pu, Zhihan Jiang, Zhuangbin Chen, Xiao He, Tieying Zhang, Jianjun Chen, Yi Li, Rui Shi, and Michael Lyu. Automated proactive logging quality improvement for large-scale codebases. InProceedings of the 2025 IEEE/ACM International Conference on Au...

    2025

  82. [90]

    Coca: Generative root cause analysis for distributed systems with code knowledge.arXiv preprint arXiv:2503.23051, 2025

    Yichen Li, Yulun Wu, Jinyang Liu, Zhihan Jiang, Zhuangbin Chen, Guangba Yu, and Michael R Lyu. Coca: Generative root cause analysis for distributed systems with code knowledge.arXiv preprint arXiv:2503.23051, 2025

    2025

  83. [91]

    Deeplv: Suggesting log levels using ordinal based neural networks

    Zhenhao Li, Heng Li, Tse-Hsun Chen, and Weiyi Shang. Deeplv: Suggesting log levels using ordinal based neural networks. In2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pages 1461–1472. IEEE, 2021

    2021

  84. [92]

    Are they all good? studying practitioners’ expectations on the readability of log messages

    Zhenhao Li, An Ran Chen, Xing Hu, Xin Xia, Tse-Hsun Chen, and Weiyi Shang. Are they all good? studying practitioners’ expectations on the readability of log messages. InProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), 2023...

    2023 doi

  85. [93]

    Detecting and explaining anomalies caused by web tamper attacks via building consistency-based normality

    Yifan Liao, Ming Xu, Yun Lin, Xiwen Teoh, Xiaofei Xie, Ruitao Feng, Frank Liaw, Hongyu Zhang, and Jin Song Dong. Detecting and explaining anomalies caused by web tamper attacks via building consistency-based normality. InProceedings of the 39th IEEE/ACM International Conferenc...

    2024

  86. [94]

    Adapting large language models for parameter-efficient log anomaly detection

    Ying Fu Lim, Jiawen Zhu, and Guansong Pang. Adapting large language models for parameter-efficient log anomaly detection. arXiv preprint, 2025. URL https://arxiv.org/abs/2503.08045. arXiv:2503.08045

    2025

  87. [95]

    Deng, and X

    Yifei Lin, H. Deng, and X. Li. Fastlogad: Log anomaly detection with mask-guided pseudo anomaly generation and discrimination,

  88. [96]

    URL https://arxiv.org/abs/2404.08750

  89. [97]

    Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, 2021

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, 2021. URL https://arxiv.org/abs/2107.13586

    2021

  90. [98]

    Interpretable online log analysis using large language models with prompt strategies

    Yilun Liu, Shimin Tao, Weibin Meng, Jingyu Wang, Wenbing Ma, Yuhang Chen, Yanqing Zhao, Hao Yang, and Yanfei Jiang. Interpretable online log analysis using large language models with prompt strategies. InProceedings of the 32nd IEEE/ACM International Conference on Program Comp...

    2024 doi

  91. [99]

    LogPrompt: Prompt engineering towards zero-shot and interpretable log analysis

    Yilun Liu, Shimin Tao, Weibin Meng, Feiyu Yao, Xingyu Zhao, and Hao Yang. LogPrompt: Prompt engineering towards zero-shot and interpretable log analysis. InCompanion Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE Companion ’24). Associa...

    2024

  92. [100]

    Loglm: From task-based to instruction-based automated log analysis

    Yilun Liu, Yuhe Ji, Shimin Tao, Minggui He, Weibin Meng, Shenglin Zhang, Yongqian Sun, Yuming Xie, Boxing Chen, and Hao Yang. Loglm: From task-based to instruction-based automated log analysis. In2025 IEEE/ACM 47th International Conference on Software Engineering: Software Eng...

    2025

  93. [101]

    Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, 2019

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach.arXiv preprint arXiv:1907.11692, 2019

    1907 arXiv

  94. [102]

    Association for Computing Machinery, New York, NY, USA, 2025

    Yang Lu.LogSage: Log Summarization Assistant with Guided Enhancement, page 1979–1981. Association for Computing Machinery, New York, NY, USA, 2025. ISBN 9798400706295. URL https://doi.org/10.1145/3672608.3707990

    1979 doi

  95. [103]

    Exploring variable potential for llm-based log parsing efficiency and reduced costs

    Wengang Lyu, Yilun Wang, Wei Wei, Runkun Xiao, Youwei Li, Gen Li, Laiyuan Yang, and Zibin Zheng. Exploring variable potential for llm-based log parsing efficiency and reduced costs. InProceedings of the FSE 2025 Companion, 2025. doi: 10 .1145/3696630.3728506. URL https://doi.o...

    2025 doi

  96. [104]

    LUK: Empowering log understanding with expert knowledge from large language models, 2024

    Lipeng Ma, Weidong Yang, Sihang Jiang, Ben Fei, Mingjie Zhou, Shuhao Li, Mingyu Zhao, Bo Xu, and Yanghua Xiao. LUK: Empowering log understanding with expert knowledge from large language models, 2024

    2024

  97. [105]

    Knowlog: Knowledge enhanced pre-trained language model for log understanding

    Lipeng Ma, Weidong Yang, Bo Xu, Sihang Jiang, Ben Fei, Jiaqing Liang, Mingjie Zhou, and Yanghua Xiao. Knowlog: Knowledge enhanced pre-trained language model for log understanding. InProceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE ’24, Ne...

    2024 doi

  98. [106]

    LogReasoner: Empowering LLMs with expert-like coarse-to-fine reasoning for log analysis tasks, 2025

    Lipeng Ma, Yixuan Li, Weidong Yang, Mingjie Zhou, Xinyi Liu, Ben Fei, Shuhao Li, Xiaoyan Sun, Sihang Jiang, and Yanghua Xiao. LogReasoner: Empowering LLMs with expert-like coarse-to-fine reasoning for log analysis tasks, 2025

    2025

  99. [107]

    Adaptivelog: An adaptive log analysis framework with the collaboration of large and small language model.ACM Transactions on Software Engineering and Methodology, 2025

    Lipeng Ma, Weidong Yang, Yixuan Li, Ben Fei, Mingjie Zhou, Shuhao Li, Sihang Jiang, Bo Xu, and Yanghua Xiao. Adaptivelog: An adaptive log analysis framework with the collaboration of large and small language model.ACM Transactions on Software Engineering and Methodology, 2025....

    2025 doi

  100. [108]

    Llmparser: An exploratory study on using large language models for log parsing

    Zeyang Ma, An Ran Chen, Dong Jae Kim, Tse-Hsun Chen, and Shaowei Wang. Llmparser: An exploratory study on using large language models for log parsing. InProceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE 2024), pages 99:1–99:13, 2024. doi: ...

    2024 doi

  101. [109]

    Librelog: Accurate and efficient unsupervised log parsing using open-source large language models

    Zeyang Ma, Dong Jae Kim, and Tse-Hsun Chen. Librelog: Accurate and efficient unsupervised log parsing using open-source large language models. InProceedings of the 47th International Conference on Software Engineering (ICSE 2025). IEEE/ACM, 2025. 42 URL https://conf.researchr....

    2025

  102. [110]

    Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models, 2023. URL https://arxiv.org/abs/2303.08896

    2023 arXiv

  103. [111]

    Optimizing software release management with gpt-enabled log anomaly detection

    Praveen Kumar Mannam. Optimizing software release management with gpt-enabled log anomaly detection. In2023 26th International Conference on Computer and Information Technology (ICCIT), pages 1–6. IEEE, 2023

    2023

  104. [112]

    Loglead-fast and integrated log loader, enhancer, and anomaly detector

    Mika V Mäntylä, Yuqing Wang, and Jesse Nyyssölä. Loglead-fast and integrated log loader, enhancer, and anomaly detector. In2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 395–399. IEEE, 2024

    2024

  105. [113]

    Using deep learning to generate complete log statements

    Antonio Mastropaolo, Luca Pascarella, and Gabriele Bavota. Using deep learning to generate complete log statements. InProceedings of the 44th International Conference on Software Engineering (ICSE ’22), pages 2279–2290, 2022. doi: 10 .1145/3510003.3511561. URL https://doi.org/...

    2022 doi

  106. [114]

    Log statements generation via deep learning: Widening the support provided to developers.Journal of Systems and Software, 210:111947, 2024

    Antonio Mastropaolo, Valentina Ferrari, Luca Pascarella, and Gabriele Bavota. Log statements generation via deep learning: Widening the support provided to developers.Journal of Systems and Software, 210:111947, 2024. doi: 10 .1016/j.jss.2023.111947. URL https: //doi.org/10.10...

    2024 doi

  107. [115]

    The effectiveness of compact fine-tuned llms in log parsing

    Maryam Mehrabi, Abdelwahab Hamou-Lhadj, and Hossein Moosavi. The effectiveness of compact fine-tuned llms in log parsing. In 2024 IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 438–448. IEEE, 2024

    2024

  108. [116]

    Augmented language models: a survey, 2023

    Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Damien Sileo, Lewis Tunstall, et al. Augmented language models: a survey, 2023. URL https://arxiv.org/abs/2302.07842

    2023 arXiv

  109. [117]

    Distributed representations of words and phrases and their compositionality.Advances in neural information processing systems, 26, 2013

    Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality.Advances in neural information processing systems, 26, 2013

    2013

  110. [118]

    Rethinking the role of demonstrations: What makes in-context learning work?, 2022

    Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. Rethinking the role of demonstrations: What makes in-context learning work?, 2022. URL https://arxiv.org/abs/2202.12837

    2022 arXiv

  111. [119]

    Crasheventllm: Predicting system crashes with large language models

    Priyanka Mudgal, Bijan Arbab, and Swaathi Sampath Kumar. Crasheventllm: Predicting system crashes with large language models. In 2024 International Conference on Information Technology and Computing (ICITCOM), pages 72–76. IEEE, 2024

    2024

  112. [120]

    Gunho No, Yukyung Lee, Hyeongwon Kang, and Pilsung Kang. RAPID: Training-free retrieval-based log anomaly detection with pre-trained language model considering token-level information.Engineering Applications of Artificial Intelligence, 133:108613, 2024. doi: 10.1016/j.engappa...

    2024 doi

  113. [121]

    Gpt-4 technical report, 2023

    OpenAI. Gpt-4 technical report, 2023. URL https://arxiv.org/abs/2303.08774

    2023 arXiv

  114. [122]

    Java platform, standard editionjava.util.logging

    Oracle. Java platform, standard editionjava.util.logging. Online documentation, 2024. URL https://docs.oracle.com/en/java/javase/

    2024

  115. [123]

    Robust and transferable anomaly detection in log data using pre-trained language models, 2021

    Harold Ott, Jasmin Bogatinovski, Alexander Acker, Sasho Nedelkoski, and Odej Kao. Robust and transferable anomaly detection in log data using pre-trained language models, 2021. URL https://arxiv.org/abs/2102.11570

    2021

  116. [124]

    Youssef Esseddiq Ouatiti, Mohammed Sayagh, Bram Adams, and Ahmed E. Hassan. Omnillp: Enhancing llm-based log level prediction with context-aware retrieval, 2025. URL https://arxiv.org/abs/2508.08545

    2025

  117. [125]

    Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, ...

    2022 arXiv

  118. [126]

    Raglog: Log anomaly detection using retrieval augmented generation, 2023

    Jonathan Pan, Swee Liang Wong, and Yidi Yuan. Raglog: Log anomaly detection using retrieval augmented generation, 2023. URL https://arxiv.org/abs/2311.05261

    2023

  119. [127]

    Bleu: a method for automatic evaluation of machine translation

    Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318, 2002

    2002

  120. [128]

    On the difficulty of training recurrent neural networks

    Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the difficulty of training recurrent neural networks. InProceedings of the 30th International Conference on Machine Learning (ICML), pages 1310–1318, 2013

    2013

  121. [129]

    Self-evolutionary group-wise log parsing based on large language model

    Changhua Pei, Zihan Liu, Jianhui Li, Erhan Zhang, Le Zhang, Haiming Zhang, Wei Chen, Dan Pei, and Gaogang Xie. Self-evolutionary group-wise log parsing based on large language model. In2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), pages 49...

    2024

  122. [130]

    Flow-of-action: Sop enhanced llm-based multi-agent system for root cause analysis

    Changhua Pei, Zexin Wang, Fengrui Liu, Zeyan Li, Yang Liu, Xiao He, Rong Kang, Tieying Zhang, Jianjun Chen, Jianhui Li, et al. Flow-of-action: Sop enhanced llm-based multi-agent system for root cause analysis. InCompanion Proceedings of the ACM on Web Conference 2025, pages 42...

    2025

  123. [131]

    Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer

    Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. InProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human...

    2018 doi

  124. [132]

    Efficiently scaling transformer inference with pagedattention,

    Reiner Pope, Yash Jain, Rohan Peri, Jeff Hankins, Baolin Lin, and Ion Stoica. Efficiently scaling transformer inference with pagedattention,

  125. [133]

    URL https://arxiv.org/abs/2309.06180. 43

    arXiv

  126. [134]

    ADALog: Adaptive unsupervised anomaly detection in logs with self-attention masked language model.arXiv preprint, 2025

    Przemek Pospieszny, Wojciech Mormul, Karolina Szyndler, and Sanjeev Kumar. ADALog: Adaptive unsupervised anomaly detection in logs with self-attention masked language model.arXiv preprint, 2025. URL https://arxiv.org/abs/2505.13496

    2025

  127. [135]

    Python logging — logging facility for python

    Python Software Foundation. Python logging — logging facility for python. Online documentation, 2024. URL https://docs.python.org/ 3/library/logging.html

    2024

  128. [136]

    Fung, Hailong Yang, Depei Qian, Jing Shang, Zhiwen Xiao, and Zhihui Wu

    Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Shu Yang, Carol J. Fung, Hailong Yang, Depei Qian, Jing Shang, Zhiwen Xiao, and Zhihui Wu. LogGPT: Exploring ChatGPT for log-based anomaly detection. In2023 IEEE International Conference on High Performance Computing & Communications, ...

    2023 doi

  129. [137]

    Pre-trained models for natural language processing: A survey.Science China Technological Sciences, 63(10):1872–1897, 2020

    Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. Pre-trained models for natural language processing: A survey.Science China Technological Sciences, 63(10):1872–1897, 2020. doi: 10 .1007/s11431-020-1647-3. URL https://arxiv .org/abs/ 2003.08271

    2020

  130. [138]

    QOS.ch. Logback. Online documentation, 2024. URL https://logback.qos.ch/

    2024

  131. [139]

    Slf4j: Simple logging facade for java

    QOS.ch. Slf4j: Simple logging facade for java. Online documentation, 2024. URL https://www.slf4j.org/

    2024

  132. [140]

    Manning, and Chelsea Finn

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model, 2023. URL https://arxiv.org/abs/2305.18290

    2023 arXiv

  133. [141]

    Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

    2020

  134. [142]

    Sentence-bert: Sentence embeddings using siamese bert-networks.arXiv preprint arXiv:1908.10084, 2019

    Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks.arXiv preprint arXiv:1908.10084, 2019

    1908 arXiv

  135. [143]

    Clogllm: A large language model enabled approach to cybersecurity log anomaly analysis

    Hengyi Ren, Kun Lan, Zhi Sun, and Shan Liao. Clogllm: A large language model enabled approach to cybersecurity log anomaly analysis. In2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC), pages 963–970. IEEE, 2024

    2024

  136. [144]

    Automated file-level logging generation for machine learning applications using llms: A case study using gpt-4o mini, 2025

    Mayra Sofia Ruiz Rodriguez, SayedHassan Khatoonabadi, and Emad Shihab. Automated file-level logging generation for machine learning applications using llms: A case study using gpt-4o mini, 2025. URL https://arxiv.org/abs/2508.04820

    2025

  137. [145]

    Toolformer: Language models can teach themselves to use tools, 2023

    Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools, 2023. URL https://arxiv.org/abs/2302.04761

    2023 arXiv

  138. [146]

    structlog: Structured logging for python

    Hynek Schlawack and contributors. structlog: Structured logging for python. Online documentation, 2024. URL https:// www.structlog.org/

    2024

  139. [147]

    Liu, and Christopher D

    Abigail See, Peter J. Liu, and Christopher D. Manning. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), volume 1, pages 1073–1083, 2017. doi: 10.18653/v1/P17-1099

    2017 doi

  140. [148]

    Virtual machine proactive fault tolerance using log-based anomaly detection.IEEE Access, 2024

    Pratheek Senevirathne, Samindu Cooray, Jerome Dinal Herath, and Dinuni Fernando. Virtual machine proactive fault tolerance using log-based anomaly detection.IEEE Access, 2024

    2024

  141. [149]

    Gpt-2c: A parser for honeypot logs using large pre-trained language models

    Febrian Setianto, Erion Tsani, Fatima Sadiq, Georgios Domalis, Dimitris Tsakalidis, and Panos Kostakos. Gpt-2c: A parser for honeypot logs using large pre-trained language models. InProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analys...

    2021 doi

  142. [150]

    Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs

    Shiwen Shan, Yintong Huo, Yuxin Su, Yichen Li, Dan Li, and Zibin Zheng. Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs. InProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 13–25, 2024

    2024

  143. [151]

    Conflogger: Enhance systems’ configuration diagnosability through configuration logging

    Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, and Zibin Zheng. Conflogger: Enhance systems’ configuration diagnosability through configuration logging. InProceedings of the 48th IEEE/ACM International Conference on Software Engineering (ICSE ’26). ACM, 2026. doi: 1...

    2026 doi

  144. [152]

    Log anomaly detection method based on bert model optimization

    Yangyi Shao, Wenbin Zhang, Peishun Liu, Ren Huyue, Ruichun Tang, Qilin Yin, and Qi Li. Log anomaly detection method based on bert model optimization. In2022 7th International conference on cloud computing and big data analytics (ICCCBDA), pages 161–166. IEEE, 2022

    2022

  145. [153]

    Reflexion: Language agents with verbal reinforce- ment learning, 2023

    Noah Shinn, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforce- ment learning, 2023. URL https://arxiv.org/abs/2303.11366

    2023 arXiv

  146. [154]

    Megatron-lm: Training multi-billion parameter language models using model parallelism, 2019

    Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. Megatron-lm: Training multi-billion parameter language models using model parallelism, 2019. URL https://arxiv.org/abs/1909.08053

    2019 arXiv

  147. [155]

    An empirical study on language models for generating log statements in test code.ACM Transactions on Software Engineering and Methodology, 2025

    Honglin Shu, Dong Wang, Antonio Mastropaolo, Gabriele Bavota, and Yasutaka Kamei. An empirical study on language models for generating log statements in test code.ACM Transactions on Software Engineering and Methodology, 2025. doi: 10.1145/3759915. 44

    2025 doi

  148. [156]

    Audit-LLM: Multi-agent collaboration for log-based insider threat detection.arXiv preprint, 2024

    Chengyu Song, Linru Ma, Jianming Zheng, Jinzhi Liao, Hongyu Kuang, and Lin Yang. Audit-LLM: Multi-agent collaboration for log-based insider threat detection.arXiv preprint, 2024. URL https://arxiv.org/abs/2408.08902

    2024

  149. [157]

    Parse-llm: A prior-free llm parser for unknown system logs

    Chengyu Song, Lin Yang, Jianming Zheng, and Fei Cai. Parse-llm: A prior-free llm parser for unknown system logs. InProceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025). ACM, 2025. doi: 10 .1145/3746252.3761363. URL https://dl...

    2025 doi

  150. [158]

    Confront insider threat: Precise anomaly detection in behavior logs based on LLM fine-tuning

    Shuang Song, Yifei Zhang, and Neng Gao. Confront insider threat: Precise anomaly detection in behavior logs based on LLM fine-tuning. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa, Barbara Di Eugenio, and Steven Schockaert, editors,Proceedings of the 31st In...

    2025

  151. [159]

    Logsense: Scalable real-time log anomaly detection architecture

    Abhay Srivatsa and Venkatasai Gudisa. Logsense: Scalable real-time log anomaly detection architecture

  152. [160]

    Bridging the gap: LLM-powered transfer learning for log anomaly detection in new software systems

    Yicheng Sui, Xiaotian Wang, Tianyu Cui, Tong Xiao, Chenghao He, Shenglin Zhang, Yuzhi Zhang, Xiao Yang, Yongqian Sun, and Dan Pei. Bridging the gap: LLM-powered transfer learning for log anomaly detection in new software systems. In41st IEEE International Conference on Data En...

    2025 doi

  153. [161]

    From alerts to intelligence: A novel LLM-aided framework for host-based intrusion detection, 2025

    Danyu Sun, Jinghuai Zhang, Jiacen Xu, Yu Zheng, Yuan Tian, and Zhou Li. From alerts to intelligence: A novel LLM-aided framework for host-based intrusion detection, 2025

    2025

  154. [162]

    SemiSMAC: A semi-supervised framework for log anomaly detec- tion with automated hyperparameter tuning.Information and Software Technology, 187:107869, 2025

    Yicheng Sun, Jacky Wai Keung, Zhen Yang, Shuo Liu, and Yihan Liao. SemiSMAC: A semi-supervised framework for log anomaly detec- tion with automated hyperparameter tuning.Information and Software Technology, 187:107869, 2025. doi: 10 .1016/j.infsof .2025.107869

    2025

  155. [163]

    Semirald: A semi-supervised hybrid language model for robust anomalous log detection.Information and Software Technology, 183:107743, July 2025

    Yicheng Sun, Jacky Wai Keung, Zhen Yang, Shuo Liu, and Hi Kuen Yu. Semirald: A semi-supervised hybrid language model for robust anomalous log detection.Information and Software Technology, 183:107743, July 2025. doi: 10 .1016/j.infsof .2025.107743. URL https://doi.org/10.1016/...

    2025 doi

  156. [164]

    Improving anomaly detection in software logs through hybrid language modeling and reduced reliance on parser.Automated Software Engineering, 33(1):12, 2026

    Yicheng Sun, Jacky Keung, Zhen Yang, Shuo Liu, and Hi Kuen Yu. Improving anomaly detection in software logs through hybrid language modeling and reduced reliance on parser.Automated Software Engineering, 33(1):12, 2026

    2026

  157. [165]

    Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. InAdvances in Neural Information Processing Systems (NeurIPS), volume 27, 2014

    2014

  158. [166]

    Domain tailored large language models for log mask prediction in cellular network diagnostics.IEEE Transactions on Network and Service Management, 22(3): 2370–2381, 2025

    Sayed Taheri, Achintha Ihalage, Prateek Mishra, Sean Coaker, Faris Muhammad, and Hamed Al-Raweshidy. Domain tailored large language models for log mask prediction in cellular network diagnostics.IEEE Transactions on Network and Service Management, 22(3): 2370–2381, 2025. doi: ...

    2025 doi

  159. [167]

    Al-bench: A benchmark for automatic logging, 2025

    Boyin Tan, Junjielong Xu, Zhouruixing Zhu, and Pinjia He. Al-bench: A benchmark for automatic logging, 2025. URL https: //arxiv.org/abs/2502.03160

    2025

  160. [168]

    Microrca-agent: Microservice root cause analysis method based on large language model agents.arXiv preprint arXiv:2509.15635, 2025

    Pan Tang, Shixiang Tang, Huanqi Pu, Zhiqing Miao, and Zhixing Wang. Microrca-agent: Microservice root cause analysis method based on large language model agents.arXiv preprint arXiv:2509.15635, 2025

    2025

  161. [169]

    Logstamp: Automatic online log parsing based on sequence labelling.ACM SIGMETRICS Performance Evaluation Review, 49(4): 93–98, 2022

    Shimin Tao, Weibin Meng, Yimeng Chen, Yichen Zhu, Ying Liu, Chunning Du, Tao Han, Yongpeng Zhao, Xiangguang Wang, and Hao Yang. Logstamp: Automatic online log parsing based on sequence labelling.ACM SIGMETRICS Performance Evaluation Review, 49(4): 93–98, 2022. doi: 10.1145/354...

    2022 doi

  162. [170]

    Biglog: Unsupervised large-scale pre-training for a unified log representation

    Shimin Tao, Yilun Liu, Weibin Meng, Zuomin Ren, Hao Yang, Xun Chen, Liang Zhang, Yuming Xie, Chang Su, Xiaosong Oiao, Weinan Tian, Yichen Zhu, Tao Han, Ying Qin, and Yun Li. Biglog: Unsupervised large-scale pre-training for a unified log representation. In 2023 IEEE/ACM 31st I...

    2023

  163. [171]

    Chethan, G

    Subin Thali, R. Chethan, G. Supreeth, M. Sangamesh, and S. Swami. LLM-based detection of cyber anomalies in industrial control systems.Zhuzao/Foundry, 28(6):237–241, 2025. doi: 10.29014/FJ-2025-1001-4977.2073

    2025 doi

  164. [172]

    Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  165. [173]

    LogGPT: Log anomaly detection via GPT.arXiv preprint, 2023

    Mohamed Trabelsi, Zenghui Yuan, Jiuyong Han, and Fatima Zohra Ros. LogGPT: Log anomaly detection via GPT.arXiv preprint, 2023. URL https://arxiv.org/abs/2309.14482

    2023

  166. [174]

    Using large language models for template detection from security event logs.International Journal of Information Security, 24, 2025

    Risto Vaarandi and Hayretdin Bahsi. Using large language models for template detection from security event logs.International Journal of Information Security, 24, 2025. doi: 10.1007/s10207-025-01018-y. URL https://doi.org/10.1007/s10207-025-01018-y

    2025 doi

  167. [175]

    Attention is all you need.Advances in neural information processing systems, 30, 2017

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

    2017

  168. [176]

    Extracting and composing robust features with denoising autoencoders

    Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. InProceedings of the 25th International Conference on Machine Learning (ICML), pages 1096–1103, 2008. doi: 10.1145/1390156.1390294

    2008 doi

  169. [177]

    Logexpert: Log-based recommended resolutions generation using large language model

    Jiabo Wang, Guojun Chu, Jingyu Wang, Haifeng Sun, Qi Qi, Yuanyi Wang, Ji Qi, and Jianxin Liao. Logexpert: Log-based recommended resolutions generation using large language model. InProceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Idea...

    2024 doi

  170. [178]

    Association for Computing Machinery, New York, NY, USA, 2025

    Kehan Wang, Siqin Zhang, Haijing Nan, Xueyu Hou, Jiaqi Zou, and Zicong Miao.Empirical Analysis of LLMDPP: Advancing Log Parsing in the LLM Era, page 753–758. Association for Computing Machinery, New York, NY, USA, 2025. ISBN 9798400714535. URL https://doi.org/10.1145/3711875.3736683

    2025 doi

  171. [179]

    Defects4log: Benchmarking llms for logging code defect detection and reasoning, 2025

    Xin Wang, Zhenhao Li, and Zishuo Ding. Defects4log: Benchmarking llms for logging code defect detection and reasoning, 2025. URL https://arxiv.org/abs/2508.11305

    2025

  172. [180]

    Self-consistency improves chain of thought reasoning in language models, 2022

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models, 2022. URL https://arxiv.org/abs/2203.11171

    2022 arXiv

  173. [181]

    Inferlog: Accelerating llm inference for online log parsing via icl-oriented prefix caching

    Yilun Wang, Pengfei Chen, Haiyu Huang, Zilong He, Gou Tan, Chuanfu Zhang, Jingkai He, and Zibin Zheng. Inferlog: Accelerating llm inference for online log parsing via icl-oriented prefix caching. InProceedings of the 48th International Conference on Software Engineering (ICSE ...

    2026

  174. [182]

    Yue Wang, Weishi Wang, Shafiq Joty, and Steven C. H. Hoi. Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8696–870...

    2021

  175. [183]

    Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models

    Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Jihong Wang, Fengbin Yin, Lunting Fan, Lingfei Wu, and Qingsong Wen. Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. InProceedings of the 33rd ACM International Conference...

    2024

  176. [184]

    Emergent abilities of large language models

    Jason Wei, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Jeff Dean, Yuhuai Li, Adam Roberts, Anna Rumshisky, Noam Shazeer, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022. URL https://arxiv.org/abs/2206.07682

    2022 arXiv

  177. [185]

    Le, and Denny Zhou

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2022. URL https://arxiv.org/abs/2201.11903

    2022 arXiv

  178. [186]

    Log parsing using llms with self-generated in-context learning and self-correction

    Yifan Wu, Siyu Yu, and Ying Li. Log parsing using llms with self-generated in-context learning and self-correction. InProceedings of the 33rd IEEE/ACM International Conference on Program Comprehension (ICPC 2025). IEEE/ACM, 2025. URL https://arxiv.org/abs/2406.03376. Method na...

    2025

  179. [187]

    Clslog: Collaborating large and small models for log-based anomaly detection

    Pei Xiao, Tong Jia, Chiming Duan, Minghua He, Weijie Hong, Xixuan Yang, Yihan Wu, Ying Li, and Gang Huang. Clslog: Collaborating large and small models for log-based anomaly detection. InCompanion Proceedings of the 33rd ACM International Conference on the Foundations of Softw...

    2025 doi

  180. [188]

    Demonstration-free: Towards more practical log parsing with large language models

    Yi Xiao, Van-Hoang Le, and Hongyu Zhang. Demonstration-free: Towards more practical log parsing with large language models. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024), pages 1243–1255. ACM, 2024. doi: 10.1145/36916...

    2024 doi

  181. [189]

    Fastlog: An end-to-end method to efficiently generate and insert logging statements

    Xiaoyuan Xie, Zhipeng Cai, Songqiang Chen, and Jifeng Xuan. Fastlog: An end-to-end method to efficiently generate and insert logging statements. InProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA ’24), pages 26–37. ACM, 2024. ...

    2024 doi

  182. [190]

    Help: Hierarchical embeddings-based log parsing

    Andy Xu and Arno Gau. Help: Hierarchical embeddings-based log parsing. arXiv preprint arXiv:2408.08300, 2024. URL https: //arxiv.org/abs/2408.08300

    2024

  183. [191]

    Unilog: Automatic logging via LLM and in-context learning

    Junjielong Xu, Ziang Cui, Yuan Zhao, Xu Zhang, Shilin He, Pinjia He, Liqun Li, Yu Kang, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, and Dongmei Zhang. Unilog: Automatic logging via LLM and in-context learning. InProceedings of the 46th IEEE/ACM International Conference on So...

    2024 doi

  184. [192]

    Divlog: Log parsing with prompt enhanced in-context learning

    Junjielong Xu, Ruichun Yang, Yintong Huo, Chengyu Zhang, and Pinjia He. Divlog: Log parsing with prompt enhanced in-context learning. InProceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE ’24), pages 199:1–199:12, 2024. doi: 10.1145/3597503....

    2024 doi

  185. [193]

    Openrca: Can large language models locate the root cause of software failures? InThe Thirteenth International Conference on Learning Representations, 2025

    Junjielong Xu, Qinan Zhang, Zhiqing Zhong, Shilin He, Chaoyun Zhang, Qingwei Lin, Dan Pei, Pinjia He, Dongmei Zhang, and Qi Zhang. Openrca: Can large language models locate the root cause of software failures? InThe Thirteenth International Conference on Learning Representations, 2025

    2025

  186. [194]

    A two-staged llm-based framework for ci/cd failure detection and remediation with industrial validation.arXiv preprint arXiv:2506.03691, 2025

    Weiyuan Xu, Juntao Luo, Tao Huang, Kaixin Sui, Jie Geng, Qijun Ma, Isami Akasaka, Xiaoxue Shi, Jing Tang, and Peng Cai. A two-staged llm-based framework for ci/cd failure detection and remediation with industrial validation.arXiv preprint arXiv:2506.03691, 2025

    2025

  187. [195]

    LogELECTRA: Self-supervised anomaly detection for unstructured logs.arXiv preprint, 2024

    Yuuki Yamanaka, Tomokatsu Takahashi, Takuya Minami, and Yoshiaki Nakajima. LogELECTRA: Self-supervised anomaly detection for unstructured logs.arXiv preprint, 2024. URL https://arxiv.org/abs/2402.10397

    2024

  188. [196]

    Rossi, Kaize Ding, Xia Hu, and Yue Zhao

    Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, and Yue Zhao. AD-LLM: Benchmarking large language models for anomaly detection. InFindings of the Association for Computational Linguistics: ACL 2025, pages...

    2025

  189. [197]

    React: Synergizing reasoning and acting in language models, 2022

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2022. URL https://arxiv.org/abs/2210.03629. 46

    2022 arXiv

  190. [198]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023. URL https://arxiv.org/abs/2305.10601

    2023 arXiv

  191. [199]

    Loggenius: An unsupervised log parsing framework with zero-shot prompt engineering

    Xian Yu, Shengxi Nong, Dongbiao He, Weijie Zheng, Teng Ma, Ning Liu, Jianhui Li, and Gaogang Xie. Loggenius: An unsupervised log parsing framework with zero-shot prompt engineering. In2024 IEEE International Conference on Web Services (ICWS), pages 1321–1328,

  192. [200]

    URL https://doi.org/10.1109/ICWS62655.2024.00159

    doi: 10.1109/ICWS62655.2024.00159. URL https://doi.org/10.1109/ICWS62655.2024.00159

    2024 doi

  193. [201]

    Logbase: A large-scale benchmark for semantic log parsing

    Chenbo Zhang, Wenying Xu, Jinbu Liu, Lu Zhang, Guiyang Liu, Jihong Guan, Qi Zhou, and Shuigeng Zhou. Logbase: A large-scale benchmark for semantic log parsing. InProceedings of the 34th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2025). ACM, 202...

    2025 doi

  194. [202]

    Semanticlog: Towards effective and efficient large-scale semantic log parsing.IEEE Transactions on Software Engineering, 2025

    Chenbo Zhang, Wenying Xu, Jinbu Liu, Lu Zhang, Guiyang Liu, Jihong Guan, Qi Zhou, and Shuigeng Zhou. Semanticlog: Towards effective and efficient large-scale semantic log parsing.IEEE Transactions on Software Engineering, 2025. doi: 10 .1109/TSE.2025.3524891. URL https://ieeex...

    2025

  195. [203]

    Aucad: Automated construction of alignment dataset from log-related issues for enhancing llm-based log generation

    Hao Zhang, Dongjun Yu, Lei Zhang, Guoping Rong, Yongda Yu, Haifeng Shen, He Zhang, Dong Shao, and Hongyu Kuang. Aucad: Automated construction of alignment dataset from log-related issues for enhancing llm-based log generation. InProceedings of the 16th International Conference...

    2025 doi

  196. [204]

    A survey of aiops in the era of large language models.ACM Computing Surveys, 2025

    Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Aiwei Liu, Yong Yang, Zhonghai Wu, Xuming Hu, Philip Yu, and Ying Li. A survey of aiops in the era of large language models.ACM Computing Surveys, 2025

    2025

  197. [205]

    Scalalog: Scalable log-based failure diagnosis using llm

    Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Hongyi Liu, and Ying Li. Scalalog: Scalable log-based failure diagnosis using llm. InICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5, 2025. doi: 10.1109/ICASSP49660.2...

    2025 doi

  198. [206]

    Xraglog: A resource-efficient and context-aware log- based anomaly detection method using retrieval-augmented generation

    Lingzhe Zhang, Tong Jia, Mengxi Jia, Yifan Wu, Hongyi Liu, and Ying Li. Xraglog: A resource-efficient and context-aware log- based anomaly detection method using retrieval-augmented generation. InAAAI 2025 Workshop on Preventing and Detecting LLM Misinformation (PDLM), 2025

    2025

  199. [207]

    Adaptive root cause localization for microservice systems with multi-agent recursion-of-thought.arXiv preprint arXiv:2508.20370, 2025

    Lingzhe Zhang, Tong Jia, Kangjin Wang, Weijie Hong, Chiming Duan, Minghua He, and Ying Li. Adaptive root cause localization for microservice systems with multi-agent recursion-of-thought.arXiv preprint arXiv:2508.20370, 2025

    2025

  200. [208]

    Bertscore: Evaluating text generation with bert.arXiv preprint arXiv:1904.09675, 2019

    Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert.arXiv preprint arXiv:1904.09675, 2019

    1904 arXiv

  201. [209]

    Logprompt: A log-based anomaly detection framework using prompts

    Ting Zhang, Xin Huang, Wen Zhao, Shaohuang Bian, and Peng Du. Logprompt: A log-based anomaly detection framework using prompts. In2023 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2023. doi: 10 .1109/IJCNN54540.2023.10191948

    2023

  202. [210]

    Leveraging rag-enhanced large language model for semi-supervised log anomaly detection

    Wanhao Zhang, Qianli Zhang, Enyu Yu, Yuxiang Ren, Yeqing Meng, Mingxi Qiu, and Jilong Wang. Leveraging rag-enhanced large language model for semi-supervised log anomaly detection. In2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), pages 168–1...

    2024 doi

  203. [211]

    Lemur: Log parsing with entropy sampling and chain-of-thought merging, 2025

    Wei Zhang, Xiangyuan Guan, Lu Yunhong, Jie Zhang, Shuangyong Song, Xianfu Cheng, Zhenhe Wu, and Zhoujun Li. Lemur: Log parsing with entropy sampling and chain-of-thought merging, 2025. URL https://arxiv.org/abs/2402.18205

    2025

  204. [212]

    Xiao Zhang, Qi Wang, Mingyi Li, Yuan Yuan, Mengbai Xiao, Fuzhen Zhuang, and Dongxiao Yu. Tamo: Fine-grained root cause analysis via tool-assisted llm agent with multi-modality observation data in cloud-native systems.IEEE Transactions on Services Computing, 18 (6):4221–4233, 2025

    2025

  205. [213]

    Automated root causing of cloud incidents using in-context learning with gpt-4

    Xuchao Zhang, Supriyo Ghosh, Chetan Bansal, Rujia Wang, Minghua Ma, Yu Kang, and Saravan Rajmohan. Automated root causing of cloud incidents using in-context learning with gpt-4. InCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software En...

    2024

  206. [214]

    LogST: Log semi-supervised anomaly detection based on sentence-BERT

    Zhaohui Zhang, Shu Chen, and Dandan Shi. LogST: Log semi-supervised anomaly detection based on sentence-BERT. In2022 6th International Conference on System Innovation (ICSIP), pages 356–361, 2022. doi: 10.1109/ICSIP55141.2022.9886069

    2022 doi

  207. [215]

    LogDAPT: Log data anomaly detection with domain- adaptive pretraining (industry track)

    Haoyu Zheng, Guojun Chu, Haifeng Sun, Jingyu Wang, Shimin Tao, and Hao Yang. LogDAPT: Log data anomaly detection with domain- adaptive pretraining (industry track). InProceedings of the 24th International Middleware Conference: Industrial Track (Middleware ’23 Industry), pages...

    2023 doi

  208. [216]

    Llm-powered zero-shot online log parsing

    Chen Zhi, Liye Cheng, Meilin Liu, Xinkui Zhao, Yueshen Xu, and Shuiguang Deng. Llm-powered zero-shot online log parsing. In2024 IEEE International Conference on Web Services (ICWS), pages 877–887. IEEE, 2024

    2024

  209. [217]

    Logparser- llm: Advancing efficient log parsing with large language models

    Aoxiao Zhong, Dengyao Mo, Guiyang Liu, Jinbu Liu, Qingda Lu, Qi Zhou, Jiesheng Wu, Quanzheng Li, and Qingsong Wen. Logparser- llm: Advancing efficient log parsing with large language models. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining...

    2024

  210. [218]

    Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, and Michael R. Lyu. Logupdater: Automated detection and repair of specific defects in logging statements.ACM Transactions on Software Engineering and Methodology, 2025. doi: 10.1145/3731754

    2025 doi

  211. [219]

    Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, and Michael R. Lyu. Larger is not always better: Exploring small open-source language models in logging statement generation.ACM Trans. Softw. Eng. Methodol., October 2025. ISSN 1049-331X. 47 doi: 10.1145...

    2025 doi

  212. [220]

    Le, and Ed Chi

    Denny Zhou, Nathanael Sch"arli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed Chi. Least-to-most prompting enables complex reasoning in large language models, 2022. URL https://arxiv .org/ abs/2205.10625

    2022 arXiv

  213. [221]

    Leveraging large language models and bert for log parsing and anomaly detection.Mathematics, 12(17), 2024

    Yihan Zhou, Yan Chen, Xuanming Rao, Yukang Zhou, Yuxin Li, and Chao Hu. Leveraging large language models and bert for log parsing and anomaly detection.Mathematics, 12(17), 2024. ISSN 2227-7390. doi: 10 .3390/math12172758. URL https://www .mdpi.com/2227- 7390/12/17/2758

    2024

  214. [222]

    Lyu, and Dongmei Zhang

    Jieming Zhu, Pinjia He, Qiang Fu, Hongyu Zhang, Michael R. Lyu, and Dongmei Zhang. Learning to log: Helping developers make informed logging decisions. InProceedings of the 37th IEEE/ACM International Conference on Software Engineering. IEEE/ACM, 2015. doi: 10.1109/ICSE.2015.60

    2015 doi

  215. [223]

    CoLA: Model collaboration for log-based anomaly detection.Proceedings of the VLDB Endowment, 18(11):3979–3987, 2025

    Xuhang Zhu, Xiu Tang, Sai Wu, Gang Chen, Haobo Wang, Chang Yao, Quanqing Xu, and Jichen Li. CoLA: Model collaboration for log-based anomaly detection.Proceedings of the VLDB Endowment, 18(11):3979–3987, 2025. doi: 10.14778/3749646.3749668

    2025 doi

  216. [224]

    Towards generalizable context-aware anomaly detection: A large-scale benchmark in cloud environments, 2025

    Xinkai Zou, Xuan Jiang, Ruikai Huang, Haoze He, Parv Kapoor, Hongrui Wu, Yibo Wang, Jian Sha, Xiongbo Shi, Zixun Huang, and Jinhua Zhao. Towards generalizable context-aware anomaly detection: A large-scale benchmark in cloud environments, 2025. URL https://arxiv.org/abs/2508.01844. 48

    2025

Pith tools

Reviewed May 21, 2026 · model on record in the stance chip above.