# Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis

arXiv: https://arxiv.org/abs/2409.13561 · HTML: https://mlsanity.com/paper/2409.13561/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 86 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 30 of 35)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [Log-based Anomaly Detection Without Log Parsing](https://mlsanity.com/paper/2108.01955/) (2021)
- [Log-based Anomaly Detection with Deep Learning: How Far Are We?](https://mlsanity.com/paper/2202.04301/) (2022)
- [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)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [Span Selection Pre-training for Question Answering](https://mlsanity.com/paper/1909.04120/) (2019)
- [Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs](https://mlsanity.com/paper/2008.09340/) (2020)
- [LILAC: Log Parsing using LLMs with Adaptive Parsing Cache](https://mlsanity.com/paper/2310.01796/) (2023)
- [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)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)
- [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)
- [Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models](https://mlsanity.com/paper/2102.11570/) (2021)
- [System Log Parsing: A Survey](https://mlsanity.com/paper/2212.14277/) (2022)
- [Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention](https://mlsanity.com/paper/2302.06914/) (2023)
- [Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study](https://mlsanity.com/paper/2307.05950/) (2023)
- [Go Static: Contextualized Logging Statement Generation](https://mlsanity.com/paper/2402.12958/) (2024)
- [Automatically Matching Bug Reports With Related App Reviews](https://mlsanity.com/paper/2102.07134/) (2021)
- [LogStamp: Automatic Online Log Parsing Based on Sequence Labelling](https://mlsanity.com/paper/2208.10282/) (2022)

## Built on by (most cited first, 6 of 6)

- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Task-Aware Reduction for Scalable LLM-Database Systems](https://mlsanity.com/paper/2510.11813/) (2025)
- [LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysis](https://mlsanity.com/paper/2601.20148/) (2026)
- [Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection](https://mlsanity.com/paper/2501.12166/) (2025)
- [MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning](https://mlsanity.com/paper/2601.07005/) (2026)

## Abstract (arXiv)

Logs are imperative in the maintenance of online service systems, which often encompass important information for effective failure mitigation. While existing anomaly detection methodologies facilitate the identification of anomalous logs within extensive runtime data, manual investigation of log messages by engineers remains essential to comprehend faults, which is labor-intensive and error-prone. Upon examining the log-based troubleshooting practices at CloudA, we find that engineers typically prioritize two categories of log information for diagnosis. These include fault-indicating descriptions, which record abnormal system events, and fault-indicating parameters, which specify the associated entities. Motivated by this finding, we propose an approach to automatically extract such faultindicating information from logs for fault diagnosis, named LoFI. LoFI comprises two key stages. In the first stage, LoFI performs coarse-grained filtering to collect logs related to the faults based on semantic similarity. In the second stage, LoFI leverages a pre-trained language model with a novel prompt-based tuning method to extract fine-grained information of interest from the collected logs. We evaluate LoFI on logs collected from Apache Spark and an industrial dataset from CloudA. The experimental results demonstrate that LoFI outperforms all baseline methods by a significant margin, achieving an absolute improvement of 25.8~37.9 in F1 over the best baseline method, ChatGPT. This highlights the effectiveness of LoFI in recognizing fault-indicating information. Furthermore, the successful deployment of LoFI at CloudA and user studies validate the utility of our method. The code and data are available at https://github.com/Jun-jie-Huang/LoFI.