mlsanityData to 14 Oct 2026

Log Parsing: How Far Can ChatGPT Go?

arXiv 2306.01590 ↗First posted 2 Jun 2023cs.SE cs.AIcited by 16

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Abstract (arXiv)

Software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

15 of 37 references.

References · 15

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  3. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  4. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  5. 2021Log-based Anomaly Detection Without Log Parsingcited by 59
  6. 2022Log-based Anomaly Detection with Deep Learning: How Far Are We?cited by 57
  7. 2018Tools and Benchmarks for Automated Log Parsingcited by 56
  8. 2023What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?cited by 47
  9. 2023Log Parsing with Prompt-based Few-shot Learningcited by 29
  10. 2022UniParser: A Unified Log Parser for Heterogeneous Log Datacited by 26
  11. 2023A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repaircited by 26
  12. 2020Self-Supervised Log Parsingcited by 23
3 more
  1. 2023Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstractioncited by 19
  2. 2021SemParser: A Semantic Parser for Log Analysiscited by 17
  3. 2022LogStamp: Automatic Online Log Parsing Based on Sequence Labellingcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 4; 2026: 4.

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  1. 2024Characterization of Large Language Model Development in the Datacentercited by 46
  2. 2025A Survey of AIOps in the Era of Large Language Modelscited by 19
  3. 2024Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logscited by 17
  4. 2024OptLLM: Optimal Assignment of Queries to Large Language Modelscited by 16
  5. 2024Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledgecited by 8
  6. 2024A Comparative Study on Large Language Models for Log Parsingcited by 5
  7. 2024Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correctioncited by 4
  8. 2024LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Modelscited by 3
  9. 2025System Log Parsing with Large Language Models: A Reviewcited by 3
  10. 2024HELP: Hierarchical Embeddings-based Log Parsingcited by 2
  11. 2025An Ethically Grounded LLM-Based Approach to Insider Threat Synthesis and Detectioncited by 1
  12. 2025LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Costcited by 0
4 more
  1. 2026SCOPE: Tree-based Self-Correcting Online Log Parsing via Syntactic-Semantic Collaborationcited by 0
  2. 2026Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluationcited by 0
  3. 2026Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detectioncited by 0
  4. 2026Legacy System Modernization with Coding Agents: A Case Studycited by 0