Sequence-to-Sequence Models for Automated Log Parsing: Transformer and Mamba Outperform Traditional Approaches
Researchers conducted a controlled empirical study comparing four sequence modeling architectures (Transformer, Mamba, LSTM variants) for automated log parsing across 396 trained models. Transformers achieved the lowest parsing error (0.111 relative Levenshtein distance), while Mamba offered competitive accuracy with substantially lower computational cost. The findings provide practical guidance for selecting models based on accuracy, computational constraints, and data availability.
A new study systematically evaluates how different sequence modeling architectures perform on automated log parsing, a critical task for software system monitoring and failure diagnosis. Researchers trained 396 models across multiple configurations using Transformer, Mamba state-space, monodirectional LSTM, and bidirectional LSTM architectures, evaluating them with relative Levenshtein edit distance and statistical significance testing. Transformers achieved the best performance with a mean relative edit distance of 0.111, followed by Mamba (0.145), mono-LSTM (0.186), and bi-LSTM (0.265). The study also found that character-level tokenization generally improves performance, sequence length has negligible practical impact on Transformer accuracy, and both Mamba and Transformer demonstrate stronger sample efficiency than recurrent models. These results show Transformers reduce parsing error by 23.4% compared to baselines, while Mamba emerges as a strong alternative when computational resources or training data are limited.
What's missing
The study does not discuss how these findings generalize to real-world log formats beyond the evaluated datasets, potential domain-specific limitations of the tested architectures, or comparison with existing rule-based or hybrid parsing approaches currently used in production systems.
What different sources said
- arXiv cs.CLCenter
On Sequence-to-Sequence Models for Automated Log Parsing
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