Browse State-of-the-Art › Log Parsing
Log Parsing
16 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Log Parsing is the task of transforming unstructured log data into a structured format that can be used to train machine learning algorithms. The structured log data is then used to identify patterns, trends, and anomalies, which can support decision-making and improve system performance, security, and reliability. The log parsing process involves the extraction of relevant information from log files, the conversion of this information into a standardized format, and the storage of the structured data in a database or other data repository.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
16 shown of 16 papers with code (29 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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17 Mar 2020 3 repositories listedThis allows the coupling of the MLM as pre-training with a downstream anomaly detection task.
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28 Feb 2024 2 repositories listedLog parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
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7 Apr 2025 1 repository listedDespite promising results, there is no structured overview of the approaches in this relatively new research field with the earliest advances published in late 2023.
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12 Oct 2024 1 repository listedAutomatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors.
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2 Aug 2024 1 repository listedIn short, LibreLog addresses privacy and cost concerns of using commercial LLMs while achieving state-of-the-arts parsing efficiency and accuracy.
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2 Jul 2024 1 repository listedThese findings provide insights into the strengths and weaknesses of LLMs in multilingual environments and the effectiveness of different prompt strategies.
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24 May 2024 1 repository listedA challenge for users lies in choosing the LLMs that best fit their needs, balancing cost and performance.
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27 Apr 2024 1 repository listedOur evaluation of 16 open-source systems shows that LLMParser achieves statistically significantly higher parsing accuracy than state-of-the-art parsers (a 96% average parsing accuracy).
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18 Aug 2023 1 repository listedAutomated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults.
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17 Aug 2023 1 repository listedWe believe our comprehensive comparison of log representation techniques can help researchers and practitioners better understand the characteristics of different log representation techniques and provide them with…
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15 Aug 2023 1 repository listedLogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380.
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2 Jun 2023 1 repository listedOur results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting.
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31 Jan 2023 1 repository listedIn order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github.
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4 Aug 2021 1 repository listedThe log parsing errors could cause the loss of important information for anomaly detection.
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12 Feb 2021 1 repository listedWe create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models.
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13 Feb 2019 1 repository listedIn many software applications, logs serve as the only interface between the application and the developer.
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