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LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation
Summary: LLMLog improves LLM-based log template generation through multi-round active annotation, selecting representative, low-confidence logs and adaptive in-context examples. An edit-distance similarity metric and keyword-covering context selection yield state-of-the-art accuracy across 16 datasets.
(summarized by gpt-5.6-luna on Jul 24 2026)
Paper ID
ha9bc171ca72aab23
Venue
VLDB
Year
2025
Pagerank
4.9769913e-05
Overall Rank
11,311 | 23.98%
DOI
10.14778/3746405.3746433
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(CC BY-NC-ND 4.0)
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Authors
1.
Fei Teng
(Hong Kong University of Science and Technology)
2.
Haoyang Li
(Hong Kong Polytechnic University)
3.
Lei Chen
(Hong Kong University of Science and Technology)
BibTeX Citation
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@article{teng_vldb25,
title = {{LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation}},
author = {Teng, Fei and Li, Haoyang and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {3134--3148},
doi = {10.14778/3746405.3746433},
url = {https://doi.org/10.14778/3746405.3746433},
year = {2025}
}
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