# ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing

arXiv: https://arxiv.org/abs/2405.13548 · HTML: https://mlsanity.com/paper/2405.13548/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [A Survey on Automated Log Analysis for Reliability Engineering](https://mlsanity.com/paper/2009.07237/) (2020)
- [xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning](https://mlsanity.com/paper/2401.07037/) (2024)
- [OWL: A Large Language Model for IT Operations](https://mlsanity.com/paper/2309.09298/) (2023)
- [UniParser: A Unified Log Parser for Heterogeneous Log Data](https://mlsanity.com/paper/2202.06569/) (2022)
- [mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture](https://mlsanity.com/paper/2404.12135/) (2024)
- [Self-Supervised Log Parsing](https://mlsanity.com/paper/2003.07905/) (2020)
- [Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://mlsanity.com/paper/2402.18205/) (2024)
- [Low-Resource Response Generation with Template Prior](https://mlsanity.com/paper/1909.11968/) (2019)
- [LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction](https://mlsanity.com/paper/2208.10833/) (2022)
- [On Automatic Parsing of Log Records](https://mlsanity.com/paper/2102.06320/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis](https://mlsanity.com/paper/2407.01896/) (2024)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)
- [ADC: Enhancing Function Calling Via Adversarial Datasets and Code Line-Level Feedback](https://mlsanity.com/paper/2412.17754/) (2024)
- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)

## Abstract (arXiv)

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.