# LogBabylon: A Unified Framework for Cross-Log File Integration and Analysis

arXiv: https://arxiv.org/abs/2412.12364 · HTML: https://mlsanity.com/paper/2412.12364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://mlsanity.com/paper/2107.05908/) (2021)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)
- [LogGPT: Log Anomaly Detection via GPT](https://mlsanity.com/paper/2309.14482/) (2023)
- [LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://mlsanity.com/paper/2408.13727/) (2024)
- [Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction](https://mlsanity.com/paper/2304.11391/) (2023)
- [Benchmarking Large Language Models for Log Analysis, Security, and Interpretation](https://mlsanity.com/paper/2311.14519/) (2023)
- [An Empirical Study of AI-based Smart Contract Creation](https://mlsanity.com/paper/2308.02955/) (2023)
- [Teaching Machines to Code: Smart Contract Translation with LLMs](https://mlsanity.com/paper/2403.09740/) (2024)
- [Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing](https://mlsanity.com/paper/2407.05202/) (2024)

## Built on by (most cited first, 1 of 1)

- [System Log Parsing with Large Language Models: A Review](https://mlsanity.com/paper/2504.04877/) (2025)

## Abstract (arXiv)

Logs are critical resources that record events, activities, or messages produced by software applications, operating systems, servers, and network devices. However, consolidating the heterogeneous logs and cross-referencing them is challenging and complicated. Manually analyzing the log data is time-consuming and prone to errors. LogBabylon is a centralized log data consolidating solution that leverages Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) technology. LogBabylon interprets the log data in a human-readable way and adds insight analysis of the system performance and anomaly alerts. It provides a paramount view of the system landscape, enabling proactive management and rapid incident response. LogBabylon consolidates diverse log sources and enhances the extracted information's accuracy and relevancy. This facilitates a deeper understanding of log data, supporting more effective decision-making and operational efficiency. Furthermore, LogBabylon streamlines the log analysis process, significantly reducing the time and effort required to interpret complex datasets. Its capabilities extend to generating context-aware insights, offering an invaluable tool for continuous monitoring, performance optimization, and security assurance in dynamic computing environments.