# Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction

arXiv: https://arxiv.org/abs/2406.03376 · HTML: https://mlsanity.com/paper/2406.03376/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 26 of its 67 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 26 of 26)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning](https://mlsanity.com/paper/2304.11384/) (2023)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [Tools and Benchmarks for Automated Log Parsing](https://mlsanity.com/paper/1811.03509/) (2018)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?](https://mlsanity.com/paper/2304.07575/) (2023)
- [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023)
- [LILAC: Log Parsing using LLMs with Adaptive Parsing Cache](https://mlsanity.com/paper/2310.01796/) (2023)
- [LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing](https://mlsanity.com/paper/2404.18001/) (2024)
- [Log Parsing with Prompt-based Few-shot Learning](https://mlsanity.com/paper/2302.07435/) (2023)
- [UniParser: A Unified Log Parser for Heterogeneous Log Data](https://mlsanity.com/paper/2202.06569/) (2022)
- [Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction](https://mlsanity.com/paper/2304.11391/) (2023)
- [SemParser: A Semantic Parser for Log Analysis](https://mlsanity.com/paper/2112.12636/) (2021)
- [Log Parsing: How Far Can ChatGPT Go?](https://mlsanity.com/paper/2306.01590/) (2023)
- [System Log Parsing: A Survey](https://mlsanity.com/paper/2212.14277/) (2022)
- [Log Parsing Evaluation in the Era of Modern Software Systems](https://mlsanity.com/paper/2308.09003/) (2023)
- [EvLog: Identifying Anomalous Logs over Software Evolution](https://mlsanity.com/paper/2306.01509/) (2023)
- [LogPTR: Variable-Aware Log Parsing with Pointer Network](https://mlsanity.com/paper/2401.05986/) (2024)

## Built on by (most cited first, 4 of 4)

- [KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis](https://mlsanity.com/paper/2602.10246/) (2026)
- [NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum](https://mlsanity.com/paper/2603.21145/) (2026)
- [MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning](https://mlsanity.com/paper/2601.07005/) (2026)
- [SLS-Bench: A Benchmark for Incident Log Summarization with Synthetic Observability Data](https://mlsanity.com/paper/2610.16412/) (2026)

## Abstract (arXiv)

Log parsing transforms log messages into structured formats, serving as a crucial step for log analysis. Despite a variety of log parsers that have been proposed, their performance on evolving log data remains unsatisfactory due to reliance on human-crafted rules or learning-based models with limited training data. The recent emergence of large language models (LLMs) has demonstrated strong abilities in understanding natural language and code, making it promising to apply LLMs for log parsing. Consequently, several studies have proposed LLM-based log parsers. However, LLMs may produce inaccurate templates, and existing LLM-based log parsers directly use the template generated by the LLM as the parsing result, hindering the accuracy of log parsing. Furthermore, these log parsers depend heavily on historical log data as demonstrations, which poses challenges in maintaining accuracy when dealing with scarce historical log data or evolving log data. To address these challenges, we propose AdaParser, an effective and adaptive log parsing framework using LLMs with self-generated in-context learning (SG-ICL) and self-correction. To facilitate accurate log parsing, AdaParser incorporates a novel component, a template corrector, which utilizes the LLM to correct potential parsing errors in the templates it generates. In addition, AdaParser maintains a dynamic candidate set composed of previously generated templates as demonstrations to adapt evolving log data. Extensive experiments on public large-scale datasets indicate that AdaParser outperforms state-of-the-art methods across all metrics, even in zero-shot scenarios. Moreover, when integrated with different LLMs, AdaParser consistently enhances the performance of the utilized LLMs by a large margin.