# Log Parsing with Prompt-based Few-shot Learning

arXiv: https://arxiv.org/abs/2302.07435 · HTML: https://mlsanity.com/paper/2302.07435/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

## Built on by (most cited first, 29 of 29)

- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [OWL: A Large Language Model for IT Operations](https://mlsanity.com/paper/2309.09298/) (2023)
- [SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents](https://mlsanity.com/paper/2403.15852/) (2024)
- [LogLLM: Log-based Anomaly Detection Using Large Language Models](https://mlsanity.com/paper/2411.08561/) (2024)
- [Log Parsing: How Far Can ChatGPT Go?](https://mlsanity.com/paper/2306.01590/) (2023)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [LogLM: From Task-based to Instruction-based Automated Log Analysis](https://mlsanity.com/paper/2410.09352/) (2024)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [LogPr'ecis: Unleashing Language Models for Automated Malicious Log Analysis](https://mlsanity.com/paper/2307.08309/) (2023)
- [Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge](https://mlsanity.com/paper/2412.01377/) (2024)
- [Log Anomaly Detection with Large Language Models via Knowledge-Enriched Fusion](https://mlsanity.com/paper/2512.11997/) (2025)
- [On the Effectiveness of Log Representation for Log-based Anomaly Detection](https://mlsanity.com/paper/2308.08736/) (2023)
- [Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis](https://mlsanity.com/paper/2409.13561/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [From Texts to Shields: Convergence of Large Language Models and Cybersecurity](https://mlsanity.com/paper/2505.00841/) (2025)
- [LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Models](https://mlsanity.com/paper/2408.01585/) (2024)
- [FastRAG: Retrieval Augmented Generation for Semi-structured Data](https://mlsanity.com/paper/2411.13773/) (2024)
- [LLMLogAnalyzer: A Clustering-Based Log Analysis Chatbot using Large Language Models](https://mlsanity.com/paper/2510.24031/) (2025)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [LanG -- A Governance-Aware Agentic AI Platform for Unified Security Operations](https://mlsanity.com/paper/2604.05440/) (2026)
- [LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics](https://mlsanity.com/paper/2604.12218/) (2026)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)
- [A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition](https://mlsanity.com/paper/2506.01147/) (2025)
- [Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language](https://mlsanity.com/paper/2506.19539/) (2025)
- [An End-to-End Framework for Functionality-Embedded Provenance Graph Construction and Threat Interpretation](https://mlsanity.com/paper/2603.17100/) (2026)
- [Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluation](https://mlsanity.com/paper/2604.00003/) (2026)
- [SpikeLogBERT: Energy-Efficient Log Parsing Using Spiking Transformer Networks](https://mlsanity.com/paper/2606.31781/) (2026)
- [SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data](https://mlsanity.com/paper/2610.16412/) (2026)

## Abstract (arXiv)

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.