# UniParser: A Unified Log Parser for Heterogeneous Log Data

arXiv: https://arxiv.org/abs/2202.06569 · HTML: https://mlsanity.com/paper/2202.06569/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2022.

## Built on by (most cited first, 26 of 26)

- [KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph](https://mlsanity.com/paper/2402.11163/) (2024)
- [LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing](https://mlsanity.com/paper/2404.18001/) (2024)
- [LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://mlsanity.com/paper/2408.13727/) (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)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study](https://mlsanity.com/paper/2409.04834/) (2024)
- [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)
- [ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing](https://mlsanity.com/paper/2405.13548/) (2024)
- [A Comparative Study on Large Language Models for Log Parsing](https://mlsanity.com/paper/2409.02474/) (2024)
- [Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction](https://mlsanity.com/paper/2406.03376/) (2024)
- [AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model](https://mlsanity.com/paper/2501.11031/) (2025)
- [LibreLog: Accurate and Efficient Unsupervised Log Parsing Using Open-Source Large Language Models](https://mlsanity.com/paper/2408.01585/) (2024)
- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)
- [HELP: Hierarchical Embeddings-based Log Parsing](https://mlsanity.com/paper/2408.08300/) (2024)
- [LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models](https://mlsanity.com/paper/2409.01909/) (2024)
- [Deep Learning-based Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2504.07839/) (2025)
- [Polymath: A Self-Optimizing Agent with Dynamic Hierarchical Workflow](https://mlsanity.com/paper/2508.02959/) (2025)
- [SchemaCoder: Automatic Log Schema Extraction Coder with Residual Q-Tree Boosting](https://mlsanity.com/paper/2508.18554/) (2025)
- [Interactive Log Parsing via Light-weight User Feedback](https://mlsanity.com/paper/2301.12225/) (2023)
- [A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition](https://mlsanity.com/paper/2506.01147/) (2025)
- [MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning](https://mlsanity.com/paper/2601.07005/) (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 provide first-hand information for engineers to diagnose failures in large-scale online service systems. Log parsing, which transforms semi-structured raw log messages into structured data, is a prerequisite of automated log analysis such as log-based anomaly detection and diagnosis. Almost all existing log parsers follow the general idea of extracting the common part as templates and the dynamic part as parameters. However, these log parsing methods, often neglect the semantic meaning of log messages. Furthermore, high diversity among various log sources also poses an obstacle in the generalization of log parsing across different systems. In this paper, we propose UniParser to capture the common logging behaviours from heterogeneous log data. UniParser utilizes a Token Encoder module and a Context Encoder module to learn the patterns from the log token and its neighbouring context. A Context Similarity module is specially designed to model the commonalities of learned patterns. We have performed extensive experiments on 16 public log datasets and our results show that UniParser outperperforms state-of-the-art log parsers by a large margin.