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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
4.9793485e-05
Overall Rank
11,303 | 24.01%
DOI
10.14778/3746405.3746433
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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
Copy BibTeX
@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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